tag:blogger.com,1999:blog-89898300353457438762024-03-13T17:34:31.185-05:00Diffusion Imaging; Introduction, tutorials and background on diffusion tensor imaging and techniquesWant to learn more about diffusion tensor imaging (DTI)? Diffusion-imaging.com provides background on the tools, methods and software to effective analyze DTI data. Posts include step by step processing and tractography tutorials. Join our discussion forum for questions and answers.Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comBlogger34125tag:blogger.com,1999:blog-8989830035345743876.post-40979865045205749732018-01-10T16:11:00.002-06:002018-02-09T13:22:02.635-06:00Calculate tract based weighted means<div style="font-size: 100%; text-align: justify;">
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<a href="https://4.bp.blogspot.com/-wl47rF9oz1k/WlZmpolYPcI/AAAAAAAAEPE/nFEjn18VJs0W590WN3F9yVPHbx5jw3rdgCLcBGAs/s1600/strip_structures_sm.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="395" data-original-width="1600" height="155" src="https://4.bp.blogspot.com/-wl47rF9oz1k/WlZmpolYPcI/AAAAAAAAEPE/nFEjn18VJs0W590WN3F9yVPHbx5jw3rdgCLcBGAs/s640/strip_structures_sm.png" width="640" /></a></div>
Extracting the weighted means of individual fiber pathways can be useful when you want to quantify the microstructure of an entire white matter structure. This is specifically useful for tract based analyses, where you run statistics on specific pathways, and not the whole brain. You can read more on the distinction between tract based and voxel based analyses <a href="http://www.diffusion-imaging.com/2012/10/voxel-based-versus-track-based.html" target="_blank">here</a>. In this article I will describe how to calculate tract based weighted means of white matter pathways in the brain.<br />
<br />
The prerequisite steps to get to tract based analyses are described in the tutorials on this website. In the <a href="http://www.diffusion-imaging.com/2015/10/dti-tutorial-1-from-scanner-to-tensor.html" target="_blank">first tutorial</a> we covered how to processed raw diffusion images and calculate tensor images. In the <a href="http://www.diffusion-imaging.com/2015/10/dti-tutorial-2-normalization-and.html" target="_blank">second tutorial</a> we described how to normalize a set of diffusion tensor images (DTI) and run statistics on the normalized brain images (including voxel bases analyses). In the <a href="http://www.diffusion-imaging.com/2012/11/fiber-tracking-tutorial-step-by-step.html" target="_blank">last tutorial</a> we demonstrated who to iteratively delineated fiber pathways of interest using anatomically defined waypoints.
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<br />
Here we will demonstrate and provide code examples on how to calculate a weighted mean scalar value for entire white matter tracts. The principle relies on using the density of tracts running through each voxel as a proportion of the total number of tracts in the volume to get a weighted estimate. Once you have a proportional index map for each fiber pathway of interest you can multiply this weighting factor by the value of the diffusion measure (e.g. FA) in that voxel, to get the weighted scalar value of each voxel. Shout out to Dr. Dan Grupe, who initiated and wrote the core of the weighted mean script. As a note, this script can also be used to extract cluster significance from voxel-wise statistical maps. See an example of this usage at the end of this post.
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<br />
The weighted mean approach allows for differential weighting of voxels within a white matter pathway that have a higher fiber count, which is most frequently observed in areas more central to the white matter tract of interest. At the same time this method will down-weigh voxels at the periphery of the tracts, in areas that often lead to partial voluming issues, where voxels not only contain white matter, but also overlap with gray matter and/or cerebrospinal fluid (CSF).
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<br />
To start off you will need a NifTI-format tract file, for example as can be exported from Trackvis. See more details on how to do this in <a href="http://www.diffusion-imaging.com/2012/11/fiber-tracking-tutorial-step-by-step.html" target="_blank">Tutorial 3</a>. You also need scalars, like FA or MD files. Overview of software packages used in this code:
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<br />
<b>TrackVis</b> by MGH<span style="text-align: start;"> (download TrackVis </span><a href="http://trackvis.org/download/" style="text-align: start;" target="_blank">here</a><span style="text-align: start;">)</span></div>
<div style="text-align: start;">
<a href="http://trackvis.org/docs/">http://trackvis.org/docs/</a><br />
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<b>fslstats</b> by FSL<span style="text-align: start;"> (download FSL </span><a href="http://fsl.fmrib.ox.ac.uk/fsldownloads/" style="text-align: start;" target="_blank">here</a><span style="text-align: start;">)</span></div>
<div style="text-align: start;">
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils</a><br />
<br />
<b>fslmaths</b> by FSL<span style="text-align: start;"> (download FSL </span><a href="http://fsl.fmrib.ox.ac.uk/fsldownloads/" style="text-align: start;" target="_blank">here</a><span style="text-align: start;">)</span></div>
<div style="text-align: start;">
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils</a><br />
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Save the weighted mean code below into a text file named "weighted_mean.sh". Make sure the file permissions for this program are set to executable by running this line after saving.
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<br />
<pre class="prettyprint linenums lang-bsh">chmod 770 weighted_mean.sh
</pre>
<h4>
Note that the code in 'weighted_mean.sh" assumes:</h4>
<ul>
<li> - A <b>base directory</b> where all folder are located with data: ${baseDir}</li>
<li> - A text file with the <b>structures</b> you want to run. Here again the naming is defined by what the name of the file is, and that it is located in the main directory, in a folder called STRUCTURES: ${baseDir}/STRUCTURES/${region}</li>
<li> - A text file with the <b>scalars</b> you want to run. The naming here is defined by how your scalar files are appended. E.g. "subj_fa.nii.gz"; in this case "fa" is the identifier of the scalar file: ${scalar_dir}/*${sub}*${scalar}.nii*</li>
<li> - The location of all the scalar files in the <b>scalar directory</b>: ${scalar_dir}
</li>
<li> - A list of <b>subject</b> prefixes that you want to run.</li>
</ul>
<br />
<h3>
Weighted mean code:</h3>
<pre class="prettyprint linenums lang-bsh">#!/bin/bash
# 2013-2018
# Dan Grupe & Do Tromp
if [ $# -lt 3 ]
then
echo
echo ERROR, not enough input variables
echo
echo Create weighted mean for multiple subjects, for multiple structures, for multiple scalars;
echo Usage:
echo sh weighted_mean.sh {process_dir} {structures_text_file} {scalars_text_file} {scalar_dir} {subjects}
echo eg:
echo
echo weighted_mean.sh /Volumes/Vol/processed_DTI/ structures_all.txt scalars_all.txt /Volumes/etc S01 S02
echo
else
baseDir=$1
echo "Output directory "$baseDir
structures=`cat $2`
echo "Structures to be run "$structures
scalars=`cat $3`
echo "Scalars to be run "$scalars
scalar_dir=$4
echo "Directory with scalars "$scalar_dir
cd ${baseDir}
mkdir -p -v ${baseDir}/weighted_scalars
finalLoc=${baseDir}/weighted_scalars
shift 4
subject=$*
echo
echo ~~~Create Weighted Mean~~~;
for sub in ${subject};
do
cd ${baseDir};
for region in ${structures};
do
img=${baseDir}/STRUCTURES/${region};
final_img=${finalLoc}/${region}_weighted;
for scalar in ${scalars};
do
#if [ ! -f ${final_img}_${sub}_${scalar}.nii.gz ];
#then
scalar_image=${scalar_dir}/*${sub}*${scalar}.nii*
#~~Calculate voxelwise weighting factor (number of tracks passing through voxel)/(total number of tracks passing through all voxels)~~
#~~First calculate total number of tracks - roundabout method because there is no 'sum' feature in fslstats~~
echo
echo ~Subject: ${sub}, Region: ${region}, Scalar: ${scalar}~
totalVolume=`fslstats ${img} -V | awk '{ print $1 }'`;
echo avgDensity=`fslstats ${img} -M`;
avgDensity=`fslstats ${img} -M`;
echo totalTracksFloat=`echo "$totalVolume * $avgDensity" | bc`;
totalTracksFloat=`echo "$totalVolume * $avgDensity" | bc`;
echo totalTracks=${totalTracksFloat/.*};
totalTracks=${totalTracksFloat/.*};
#~~Then divide number of tracks passing through each voxel by total number of tracks to get voxelwise weighting factor~~
echo fslmaths ${img} -div ${totalTracks} ${final_img};
fslmaths ${img} -div ${totalTracks} ${final_img};
#~~Multiply weighting factor by scalar of each voxel to get the weighted scalar value of each voxel~~
echo fslmaths ${final_img} -mul ${scalar_image} -mul 10000 ${final_img}_${sub}_${scalar};
fslmaths ${final_img} -mul ${scalar_image} -mul 10000 ${final_img}_${sub}_${scalar};
#else
# echo "${region} already completed for subject ${sub}";
#fi;
#~~Sum together these weighted scalar values for each voxel in the region~~
#~~Again, roundabout method because no 'sum' feature~~
echo totalVolume=`fslstats ${img} -V | awk '{ print $1 }'`;
totalVolume=`fslstats ${img} -V | awk '{ print $1 }'`;
echo avgWeightedScalar=`fslstats ${final_img}_${sub}_${scalar} -M`;
avgWeightedScalar=`fslstats ${final_img}_${sub}_${scalar} -M`;
value=`echo "${totalVolume} * ${avgWeightedScalar}"|bc`;
echo ${sub}, ${region}, ${scalar}, ${value} >> ${final_img}_output.txt;
echo ${sub}, ${region}, ${scalar}, ${value};
#~~ Remember to divide final output by 10000 ~~
#~~ and tr also by 3 ~~
rm -f ${final_img}_${sub}_${scalar}*.nii.gz
done;
done;
done;
fi
</pre>
</div>
Once the weighted mean program is saved in a file you can start running code to run groups of subjects. See for example the script below.
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<h3>
Run Weighted Mean for group of subjects:</h3>
<pre class="prettyprint linenums lang-bsh">#Calculate weighted means:
echo fa tr ad rd > scalars_all.txt
echo CING_L CING_R UNC_L UNC_R > structures_all.txt
sh /Volumes/Vol/processed_DTI/SCRIPTS/weighted_mean.sh /Volumes/Vol/processed_DTI/ structures_all.txt scalars_all.txt /Volumes/Vol/processed_DTI/scalars S01 S02 S03 S04;
</pre>
Once that finishes running you can organize the output data, and divide the output values by 10000. This is necessary because to make sure the output values in the weighted mean code have sufficient number of decimals they are multiplied by 10000. Furthermore, this code will also divide trace (TR) values by 3 to get the appropriate value of mean diffusivity (MD = TR/3).
<br />
<h3>
Organize output data:</h3>
<pre class="prettyprint linenums lang-bsh">cd /Volumes/Vol/processed_DTI/weighted_scalars
for scalar in fa tr ad rd;
do
for structure in CING_L CING_R UNC_L UNC_R;
do
rm -f ${structure}_${scalar}_merge.txt;
echo "Subject">>subject${scalar}${structure}.txt;
echo ${structure}_${scalar} >> ${structure}_${scalar}_merge.txt;
for subject in S01 S02 S03 S04;
do
echo ${subject}>>subject${scalar}${structure}.txt;
if [ "${scalar}" == "tr" ]
then
var=`cat *_weighted_output.txt | grep ${subject}|grep ${structure}|grep ${scalar}|awk 'BEGIN{FS=" "}{print $4}'`;
value=`bc <<<"scale=8; $var / 30000"`
echo $value >> ${structure}_${scalar}_merge.txt;
else
var=`cat *_weighted_output.txt | grep ${subject}|grep ${structure}|grep ${scalar}|awk 'BEGIN{FS=" "}{print $4}'`;
value=`bc <<<"scale=8; $var / 10000"`
echo $value >> ${structure}_${scalar}_merge.txt;
fi
done
mv subject${scalar}${structure}.txt subject.txt;
cat ${structure}_${scalar}_merge.txt;
done
done
#Print data to text file and screen
rm all_weighted_output_organized.txt
paste subject.txt *_merge.txt > all_weighted_output_organized.txt
cat all_weighted_output_organized.txt
</pre>
This should provide you with a text file with columns for each structure & scalar combination, with rows for each subject. You can then export this to your favorite statistical processing software.
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Finally as promised. Code to extract significant cluster from whole brain voxel-wise statistics, in this case from FSL's Randomise output:
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<h3>
Extract binary files for each significant clusters:</h3>
<pre class="prettyprint linenums lang-bsh">#Extract Cluster values for all significant maps
dir=/Volumes/Vol/processed_DTI/
cd $dir;
rm modality_index.txt;
for study in DTI/STUDY_randomise_out DTI/STUDY_randomise_out2;
do
prefix=`echo $study|awk 'BEGIN{FS="randomise_"}{print $2}'`;
cluster -i ${study}_tfce_corrp_tstat1 -t 0.95 -c ${study}_tstat1 --oindex=${study}_cluster_index1;
cluster -i ${study}_tfce_corrp_tstat2 -t 0.95 -c ${study}_tstat2 --oindex=${study}_cluster_index2;
num1=`fslstats ${study}_cluster_index1.nii.gz -R|awk 'BEGIN{FS=" "}{print $2}'|awk 'BEGIN{FS="."}{print $1}'`;
num2=`fslstats ${study}_cluster_index2.nii.gz -R|awk 'BEGIN{FS=" "}{print $2}'|awk 'BEGIN{FS="."}{print $1}'`
echo $prefix"," $num1 "," $num2;
echo $prefix"," $num1 "," $num2>>modality_index.txt;
#loop through significant clusters
count=1
while [ $count -le $num1 ];
do
fslmaths ${study}_cluster_index1.nii.gz -thr $count -uthr $count -bin /Volumes/Vol/processed_DTI/STRUCTURES/${prefix}_${count}_neg.nii.gz;
let count=count+1
done
count=1
while [ $count -le $num2 ];
do
fslmaths ${study}_cluster_index2.nii.gz -thr $count -uthr $count -bin /Volumes/Vol/processed_DTI/STRUCTURES/${prefix}_${count}_pos.nii.gz;
let count=count+1
done
done
</pre>
<h3>
Extract cluster means:</h3>
<pre class="prettyprint linenums lang-bsh">#Extract TFCE cluster means
rm -f *_weighted.nii.gz
rm -f *_weighted_output.txt;
rm -f *_merge.txt;
cd /Volumes/Vol/processed_DTI;
for i in DTI/STUDY_randomise_out DTI/STUDY_randomise_out2;
do
prefix=`echo ${i}|awk 'BEGIN{FS="/"}{print $1}'`;
suffix=`echo ${i}|awk 'BEGIN{FS="/"}{print $2}'`;
rm structures_all.txt;
cd /Volumes/Vol/processed_DTI/STRUCTURES;
for j in `ls ${prefix}_${suffix}*`;
do
pre=`echo ${j}|awk 'BEGIN{FS=".nii"}{print $1}'`;
echo $pre >> /Volumes/Vol/processed_DTI/structures_all.txt;
done
cd /Volumes/Vol5/processed_DTI/NOMOM2/TEMPLATE/normalize_2017;
rm scalars_all.txt;
echo $suffix > scalars_all.txt;
sh ./weighted_mean.sh /Volumes/Vol/processed_DTI/ structures_all.txt scalars_all.txt /Volumes/Vol/processed_DTI/${prefix}/${suffix} S01 S02 S03 S04;
done
</pre>
Finally, just run the code to organize the output data.
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<br />
This code has now helped you extract weighted mean values for your selected pathways and clusters. You can now import these files into CSV files and run statistics in your favorite statistical package (e.g. python on R). <br />
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<br />
<br />Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-84228418977082777612015-11-30T13:02:00.000-06:002016-10-14T12:46:58.974-05:00A guide to quantifying head motion in DTI studies<div style="font-size: 14px;">
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<a href="http://www.mghradrounds.org/index.php?src=gendocs&link=2009_june" style="margin-left: auto; margin-right: auto;" target="_blank"><img alt="Different modality, same problem; Axial FLAIR images of the brain obtained before (A) and after (B) application of a real-time motion-correction algorithm. (MGH)" border="0" height="361" src="https://2.bp.blogspot.com/-ESV5CaEQfNM/VkTbIvRkWcI/AAAAAAAADjc/cAfj3reNj3g/s640/figure2.jpg" title="Different modality, same problem; Axial FLAIR images of the brain obtained before (A) and after (B) application of a real-time motion-correction algorithm. (MGH)" width="640" /></a></div>
<div class="separator" style="clear: both; font-size: 14px; text-align: center;">
<span id="goog_742759227"></span><span id="goog_742759228"></span><a href="https://www.blogger.com/"></a></div>
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<div style="text-align: justify;">
It is unfortunately not common practice in diffusion imaging research to report on observed head motion of the participants in the scanner. Even though head motion can significantly influence study results (see also <a href="http://www.sciencedirect.com/science/article/pii/S1053811913011312" target="_blank">Yendiki <i>et al</i>., 2014</a>). A recent publication by Weinberger and Radulescu (<a href="http://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2015.15060753" target="_blank">2015</a>) reiterated the importance of controlling for unintended covariates in neuroimaging research, they note that:</div>
<br />
<blockquote class="tr_bq" style="text-align: justify;">
"The widespread use of MRI has led to a wealth of structural and functional anatomical findings in patients with diverse psychiatric disorders that may represent insights into pathobiology. However, recent technical reports indicate that data from popular MRI research—particularly structural MRI, resting-state functional MRI, and diffusion tensor imaging—are highly sensitive to common artifacts (e.g., head motion and breathing effects) that may dominate the results. Because these and other important confounders of MRI data (e.g., smoking, body weight, metabolic variations, medical comorbidities, psychoactive drugs, alcohol use, mental state) tend to vary systematically between patient and control groups, the evidence that findings are neurobiologically meaningful is inconclusive and may represent artifacts or epiphenomena of uncertain value. The authors caution that uncritically accepting from study to study findings that may represent fallacies of all sorts carries the risk of misinforming practitioners and patients about biological abnormalities underlying psychiatric illness." - <a href="http://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2015.15060753" target="_blank">AJP 2015</a></blockquote>
<div style="text-align: justify;">
One of the main artifacts mentioned is head motion, and although it is not commonly reported in DTI studies, it is possible to quantify this variable and test group differences. This guide should help you in this process and hopefully incentivize DTI studies to report on this nuisance variable. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
After running motion and eddy correction using FSL's <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Eddy_Current_Correction" target="_blank">eddy_correct</a> tool, you can extract how much the data had to be moved to counter head motion using the .ecclog output. <a href="http://users.fmrib.ox.ac.uk/~mark/" target="_blank">Mark Jenkinson</a> wrote some code to extract these values, and posted it on the FSL mail list, you can find his post <a href="https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=fsl;e2a42b99.1101" target="_blank">here</a> and copy his code.</div>
<div style="text-align: justify;">
In order to run that code for a group of subjects you can use the wrapper code below. The output will be text and image files with the displacement, rotation and translation information for each subject.
</div>
<pre class="prettyprint linenums lang-bsh">echo ~~~Export displacement, rotation and translation for each subject~~~
for i in `ls *.ecclog`
do
prefix=`echo $i | awk 'BEGIN{FS=".ecclog"}{print $1}'`
ec_plot.sh ${i}
mv ec_disp.txt ${prefix}_ec_disp.txt
mv ec_disp.png ${prefix}_ec_disp.png
mv ec_rot.txt ${prefix}_ec_rot.txt
mv ec_rot.png ${prefix}_ec_rot.png
mv ec_trans.txt ${prefix}_ec_trans.txt
mv ec_trans.png ${prefix}_ec_trans.png
done
</pre>
<div style="text-align: justify;">
The ec_disp files contain the eddy current estimated mean displacement (mm). The ec_rot files contain the eddy current estimated rotations (radians). And the ec_trans files contain the eddy current estimated translations (mm). In order to calculate the <a href="https://en.wikipedia.org/wiki/Euclidean_distance">euclidian distance</a> from these rotations for each subject I wrote the below code:</div>
<br />
<pre class="prettyprint linenums lang-bsh">Echo ~~~Extract absolute translation average (mm) and euclidian distance~~~
for i in `ls *_ec_trans.txt`;
do
prefix=`echo $i | awk 'BEGIN{FS="_ec_trans"}{print $1}'`
count=`cat $i| wc |awk '{ print $1}'`
#Translation for x
x_total=`cat $i |tr -d - |awk '{s+=$1} END {print s}'`
x_average=`echo "scale=4; $x_total / $count" | bc`
#Translation for y
y_total=`cat $i |tr -d - |awk '{s+=$2} END {print s}'`
y_average=`echo "scale=4; $y_total / $count" | bc`
#Translation for z
z_total=`cat $i |tr -d - |awk '{s+=$3} END {print s}'`
z_average=`echo "scale=4; $z_total / $count" | bc`
#euclidian distance
euclidian=$(echo "scale=4;sqrt(($x_average*$x_average)+($y_average*$y_average)+($z_average*$z_average))" | bc)
echo ${prefix}, ${x_average}, ${y_average}, ${z_average}, ${euclidian} >> movement_data.txt
echo ${prefix}, ${x_average}, ${y_average}, ${z_average}, ${euclidian}
done
</pre>
<div style="text-align: justify;">
This code takes the absolute value of the x, y and z translation and calculates the average for each, and then uses these values to calculate total euclidian distance per subject. The output will be dumped on the screen and written to movement_data.txt. Once you have these data, you can calculate if there is a significant group difference in movement parameters. So you can report this data in your work.<br />
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<span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">---------------------</span><br />
Reference this post as:<span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;"> Do Tromp, A guide to quantifying head motion in DTI studies,</span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; vertical-align: baseline;">The Winnower </em><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">3:e146228.88496 (2016). DOI:</span><a href="https://dx.doi.org/10.15200/winn.146228.88496" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; text-decoration: none; vertical-align: baseline;">10.15200/winn.146228.88496</a></div>
Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-22007821976271361042015-10-13T12:21:00.003-05:002018-01-19T16:12:27.144-06:00DTI Tutorial 2 - Normalization and Statistics<br />
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</div>
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<a href="http://3.bp.blogspot.com/-yEkZxuBJhgQ/Vh0-3r621bI/AAAAAAAADeM/7lUkvBHyEhg/s1600/2-tutorial-make.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="272" src="https://3.bp.blogspot.com/-yEkZxuBJhgQ/Vh0-3r621bI/AAAAAAAADeM/7lUkvBHyEhg/s640/2-tutorial-make.jpg" width="640" /></a></div>
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<div style="text-align: justify;">
In our <a href="http://www.diffusion-imaging.com/2015/10/dti-tutorial-1-from-scanner-to-tensor.html" target="_blank">first tutorial</a> we processed our raw images and calculated tensor images. In this second tutorial we will address how to normalize a set of diffusion tensor images (DTI) and then run statistics on the normalized brain images. First it is important to understand why you have to normalize brain images; lets see what Wikipedia has to say about spatial normalization:<br />
<br />
<blockquote class="tr_bq">
"In neuroimaging, spatial normalization is an image processing step, more specifically an image registration method. Human brains differ in size and shape, and one goal of spatial normalization is to deform human brain scans so one location in one subject's brain scan corresponds to the same location in another subject's brain scan." - <a href="https://en.wikipedia.org/wiki/Spatial_normalization" target="_blank">Wikipedia</a></blockquote>
</div>
<div style="text-align: justify;">
As you see, inter-individual statistics will only be effective when brain images are in the same space, and spatial normalization can take care of that. An important distinction between diffusion tensor imaging and other imaging modality (like T1 anatomicals or fMRI scans), is that DTI allows for a more sophisticated normalization. Where T1 and fMRI scans are normalized based on intensity scales, diffusion tensor images instead offer the ability to register tensors, that have both length and direction. The property thus allows for a better capability to register white matter structures that have highly anisotropic tensors. In <a href="http://www.dailymotion.com/video/xjzxat_ismrm-2011-tutorial-group-analysis-atlases_tech" target="_blank">this</a> video the developer of a publicly available tensor based normalization toolbox, Gary Zhang, discusses why tensor-based normalization can be better than other normalization methods, what statistical analyses you can run after normalization, and the difference between tract based spatial statistics (TBSS) and tract specific analysis (TSA). The below image details some of the advantages. For example in the area's with the yellow circles, you see areas of the brain where T1 anatomicals would have a hard time effectively registering inter-subjects anatomy for different white matter tracts, because the contrast in that region is equal despite it including multiple tracts with different orientation. Using the tensor information as show on the right will help discriminate between those regions, and allow for a more advanced registration between subjects.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><span style="margin-left: auto; margin-right: auto;"><a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Main.HomePage" target="_blank"><img border="0" height="400" src="https://2.bp.blogspot.com/-A47hNC5ua4o/VhQwiNjOQRI/AAAAAAAADXc/h1Q_p6EN590/s400/WM_morphometry_with_DTITK.png" width="283" /></a></span></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Advantage of tensor specific normalization. Image by <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Main.HomePage" target="_blank">DTI-TK</a></td></tr>
</tbody></table>
In this tutorial there will be a number of steps that we have to run through in order to test statistical models, these steps are detailed below:</div>
<div style="text-align: justify;">
1. Make sure the tensor files are in the right format for tensor normalization.</div>
<div style="text-align: justify;">
2. Make population template by normalizing tensor images</div>
<div style="text-align: justify;">
3. Register population template to MNI space<br />
4. Produce scalar images (FA, MD, AD, RD) and check quality<br />
5. Run whole brain voxel-wise statistics<br />
6. Share your data.</div>
<div style="text-align: justify;">
<br />
You can look through slides that go into some of the background of this tutorial <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/DTI_Tutorial_2_background.pdf" target="_blank">here</a>. Throughout the tutorial there will be questions to help with a better understanding of each of the steps and introduce you to the software that is being used. This might require installing said software and downloading example data from <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">my website</a>. At the end of the document you will find a pdf with the answers to the questions in this tutorial, although try to figure things out yourself as much as possible. If you run into issues going through this tutorial please direct your questions to the <a href="http://www.diffusion-imaging.com/2013/03/diffusion-imaging-discussion-forum.html" target="_blank">diffusion imaging forum</a>, where there will likely be someone able to help you out. Please make sure to describe your question as clearly as possible. Remember to please direct software specific questions to their own respective forums or mail lists.</div>
<div style="text-align: justify;">
<br /></div>
<h2>
1. Standardize Tensor Images for Normalization</h2>
<div style="text-align: justify;">
To get the tensor images ready for normalization we will have to make sure that tensors:<br />
- have the right diffusivity unit,<br />
- remove extreme outliers,<br />
- and are symmetric and positive-definite.</div>
<div style="text-align: justify;">
These steps are described in detail on the <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.BeforeReg" target="_blank">website of DTI-TK</a>; which is the website of the software package that we will use for the tensor normalization. </div>
<div>
<br />
Example code:<br />
<pre class="prettyprint linenums lang-bsh">i=052212_s09_dti_eddy_fm_strip_dt.nii
prefix=052212_s09_dti_eddy_fm_strip
echo ~~~Adjusting Diffusivity Units~~~
TVtool -in $i -scale 1000000000 -out ${prefix}_sdt.nii.gz
echo ~~Checking for and removing Outliers~~
TVtool -in ${prefix}_sdt.nii.gz -norm
SVtool -in ${prefix}_sdt_norm.nii.gz -stats
BinaryThresholdImageFilter ${prefix}_sdt_norm.nii.gz ${prefix}_non_outliers.nii.gz 0 100 1 0
TVtool -in ${prefix}_sdt.nii.gz -mask ${prefix}_non_outliers.nii.gz -out ${prefix}_tmp.nii.gz
mv -f ${prefix}_tmp.nii.gz ${prefix}_sdt.nii.gz
TVtool -in ${prefix}_sdt.nii.gz -norm
echo ~Stats for${prefix} - max should be below 100~
SVtool -in ${prefix}_sdt_norm.nii.gz -stats
echo ~~~Enforcing positive semi-definiteness~~~
TVtool -in ${prefix}_sdt.nii.gz -spd -out ${prefix}_tmp.nii.gz
mv -f ${prefix}_tmp.nii.gz ${prefix}_sdt.nii.gz
echo ~~~Standardizing Voxel Space~~~
TVAdjustVoxelspace -in ${prefix}_sdt.nii.gz -origin 0 0 0 -out ${prefix}_sdt.nii.gz
echo ~~~Resample to isotropic voxels~~~
TVResample -in ${prefix}_sdt.nii.gz -align center -size 256 256 128 -vsize 1 1 1
rm -f ${prefix}_tmp.nii.gz
rm -f ${prefix}_non_outliers.nii.gz
rm -f ${prefix}_sdt_nonSPD.nii.gz
rm -f ${prefix}_sdt_norm.nii.gz
</pre>
<div style="text-align: justify;">
<i>Q1: Go through the <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.BeforeReg" target="_blank">DTI-TK preprocessing tutorial</a>, indicate which lines of the above code correspond to which steps in the outline. What is the goal of each step? When you use this code make sure you use the right diffusivity units, the code above is just an example, find out how to convert your own <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.Diffusivity" target="_blank">here</a>.</i><br />
<br />
This code uses a number of new tools. One of which will be of interest to take a closer look at.<br />
<br />
<b>TVtool</b> by DTI-TK (download <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=SourceCode.SourceCode" target="_blank">here</a>)<br />
<a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.TVtool" target="_blank">http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.TVtool</a><br />
<br />
<i>Q2: Using TVtool investigate how and then write the code to produce the axial diffusivity (AD) map from the standardized tensor file (download 052212_s09_dti_eddy_fm_strip_sdt.nii.gz <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">here</a>). What other maps can that tool help produce?</i><br />
<i><br /></i>
<i>Q3: What are the 4 main diffusion measurements that are often used in DTI? Take a look at the explanation on <a href="http://www.diffusion-imaging.com/2015/10/what-is-diffusion-tensor.html" target="_blank">this page</a>, and describe which measure you would pick if you were selectively interested in white matter?</i><br />
<i><br /></i>
<br />
<h4>
Additional online resources:</h4>
</div>
<div>
- More info on how to calculate the diffusion tensor <a href="http://www.diffusion-imaging.com/2014/04/from-diffusion-weighted-images-to.html" target="_blank">here</a><br />
- Articles demonstrating improved registration when using DTI-TK <a href="http://dx.doi.org/10.1016/j.neuroimage.2011.01.038" target="_blank">here</a> and <a href="http://dx.doi.org/10.1016/j.neuroimage.2011.07.029" target="_blank">here</a><br />
<br /></div>
<div>
<h2>
2. Normalize Tensors Images</h2>
In the next step we will take the standardized tensor images for each individual subject and run a multi-step normalization. The steps of the normalization are detailed on the <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.Registration" target="_blank">DTI-TK website</a>, but essentially consists of four steps:<br />
1. Make initial mean<br />
2. Run a rigid registration<br />
3. Run an affine registration<br />
4. Run a diffeomorphic registration.<br />
Each normalization step iteratively moves each individual image closer to the population mean. As described by DTI-TK:<br />
<br />
<blockquote class="tr_bq">
<div style="text-align: justify;">
"Rigid alignment aims to find a rigid transformation, a linear transformation that does not alter objects size and shape, that best aligns two objects in question. (...) Affine alignment improves upon rigid alignment by finding a more general linear transformation that allows objects' global size and shape to be altered. (...) After you've aligned your DTI volumes affinely, you can proceed with the deformable alignment, which is designed to improve alignment quality by removing size or shape differences between local structures." - <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.Registration" target="_blank">DTI-TK</a></div>
</blockquote>
</div>
<span style="text-align: justify;">The below figure shows how the steps are applied and combined.</span><br />
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: -webkit-standard; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: justify; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px;">
</div>
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="-webkit-text-stroke-width: 0px; font-family: -webkit-standard; letter-spacing: normal; margin-bottom: 0.5em; margin-left: auto; margin-right: auto; orphans: auto; padding: 6px; text-align: center; text-indent: 0px; text-transform: none; widows: auto; word-spacing: 0px;"><tbody>
<tr><td><div style="margin: 0px;">
<a href="http://3.bp.blogspot.com/-YooXU4k6ons/VhgfzIeLPgI/AAAAAAAADbA/TTp0oTTS1O0/s1600/dtitk_mapping_subject_to_template.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="247" src="https://3.bp.blogspot.com/-YooXU4k6ons/VhgfzIeLPgI/AAAAAAAADbA/TTp0oTTS1O0/s400/dtitk_mapping_subject_to_template.png" width="400" /></a></div>
</td></tr>
<tr><td class="tr-caption" style="font-size: 13px; padding-top: 4px;"><div style="margin: 0px;">
<div style="text-align: center;">
Iterative normalization process. Image by <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Main.HomePage" target="_blank">DTI-TK</a></div>
</div>
</td></tr>
</tbody></table>
<div style="text-align: justify;">
This process starts by averaging all the individual tensor images to create a starting point template, a population template that has yet to be normalized, the example FA from these tensor images may look something like this:</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-vRxdC2odte0/Vhgcz0eODvI/AAAAAAAADa0/V1COF_xylsE/s1600/mean%2Binitial%2BFA.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="180" src="https://1.bp.blogspot.com/-vRxdC2odte0/Vhgcz0eODvI/AAAAAAAADa0/V1COF_xylsE/s640/mean%2Binitial%2BFA.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example of un-normalized average FA of population of images</td></tr>
</tbody></table>
<div style="text-align: justify;">
Then as a next step each individual gets registered to this new population template iteratively. This process consists of rigid, affine and diffeomorphic registrations. A rigid-body transformation in three dimensions (x, y and z) is defined by six parameters: three translations and three rotations (<a href="http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/pdfs/Ch2.pdf" target="_blank">source</a>).<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/--c8mmap-qLI/Vi7MRXMCJeI/AAAAAAAADfY/ku_IH2yA_Jw/s1600/Screen%2BShot%2B2015-10-26%2Bat%2B7.58.00%2BPM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="303" src="https://3.bp.blogspot.com/--c8mmap-qLI/Vi7MRXMCJeI/AAAAAAAADfY/ku_IH2yA_Jw/s320/Screen%2BShot%2B2015-10-26%2Bat%2B7.58.00%2BPM.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Affine includes 12 parameter transformation: 3 rotations, 3 translations, 3 sheers, 3 zooms</td></tr>
</tbody></table>
These transformations are a subset of the 12 transformations allowed in an affine registration of a three dimensional volume, which besides the rotation and translation also includes three sheers, and three zooms. Although affine registrations still have the constraint that parallel lines remain parallel after registration, thus it is contained to a linear registration. Or as wiki puts it:</div>
<blockquote class="tr_bq">
<div style="text-align: justify;">
"In geometry, an affine transformation, affine map or an affinity (from the Latin, affinis, "connected with") is a function between affine spaces which preserves points, straight lines and planes. Also, sets of parallel lines remain parallel after an affine transformation. An affine transformation does not necessarily preserve angles between lines or distances between points, though it does preserve ratios of distances between points lying on a straight line." - <a href="https://en.wikipedia.org/wiki/Affine_transformation" target="_blank">Wikipedia</a></div>
</blockquote>
<div style="text-align: justify;">
Finally the <a href="https://en.wikipedia.org/wiki/Diffeomorphism" target="_blank">diffeomorphic</a> or deformable registration removes those constraints too and allows for a non-linear higher dimension registration, as long as it is invertible.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://nipy.org/dipy/examples_built/syn_registration_2d.html" style="margin-left: auto; margin-right: auto;" target="_blank"><img border="0" height="201" src="https://1.bp.blogspot.com/-WxieV-oe-mg/VhkkW5OqtzI/AAAAAAAADbg/9zSXk4DVr9M/s400/diffeomorphic_map.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Examples of diffeomorphic transformations</td></tr>
</tbody></table>
</div>
Example script:
<br />
<pre class="prettyprint linenums lang-bsh">
echo ~~~Create an initial mean~~~;
for spd in `ls *sdt.nii.gz`;
do
subj_list_file=subj_list;
echo $spd >> subj_list_file.txt;
done
TVMean -in $subj_list_file.txt -out mean_initial.nii.gz;
TVResample -in mean_initial.nii.gz -vsize 1 1 1 -size 256 256 128;
echo ~~~Rigid alignment with initial mean~~~;
sh dti_rigid_population mean_initial.nii.gz ${subj_list_file}.txt EDS 3;
echo ~~~Affine alignment with output from rigid alignment~~~;
sh dti_affine_population mean_rigid3.nii.gz ${subj_list_file}.txt EDS 3;
echo ~~~Diffeomorphic alignment with output from affine alignment~~~;
TVtool -tr -in mean_affine3.nii.gz;
BinaryThresholdImageFilter mean_affine3_tr.nii.gz mask.nii.gz 0 .01 100 1 0;
sh dti_diffeomorphic_population mean_affine3.nii.gz ${subj_list_file}_aff.txt mask.nii.gz 0.002</pre>
<br />
<div style="text-align: justify;">
<i>Q4: </i><i>Which step do you think will take the most time to run? </i><i>Note the step where you resample your image to have isotropic voxel size, which line is this? Read <a href="http://www.diffusion-imaging.com/2015/03/non-isotropic-voxels-what-to-do-and-why.html" target="_blank">this post</a> on the importance, explain why we need isotropic voxels for fiber tracking. Resampling also alters the image dimensions, when you change those, you can read <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.Registration" target="_blank">here</a> that DTI-TK needs your image dimensions to be <a href="https://en.wikipedia.org/wiki/Power_of_two" target="_blank">powers of 2</a>. What are most likely often used powers of 2 in neuroimaging volume dimensions?</i><br />
<br /></div>
<h4>
Additional online resources:</h4>
- Courses on fMRI spatial normalization by Wellcome Trust centre <a href="http://www.fil.ion.ucl.ac.uk/spm/course/slides10-vancouver/01_Spatial_Preprocessing.pdf" target="_blank">here</a> and <a href="http://www.fil.ion.ucl.ac.uk/spm/doc/mfd/2009/Spatial.ppt" target="_blank">here</a><br />
- Courses on fMRI spatial normalization by SPM <a href="http://www.mccauslandcenter.sc.edu/mricro/mricro/mritut.html" target="_blank">here</a><br />
- <a href="http://stnava.github.io/ANTs/" target="_blank">Advanced normalization tools (ANTS)</a> for normalization of scalar images<br />
- <a href="http://scil.dinf.usherbrooke.ca/wp-content/courses/imn530/ants.pdf" target="_blank">Background documentation</a> on normalization by ANTS<br />
- Further documentation from <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.FirstRegistration" target="_blank">DTI-TK on tensor normalization</a>
<br />
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<h2>
3. Register population template to MNI-atlas</h2>
<div>
Below you can see the result of the population mean post-normalization. At this stage you are at a cross-roads of options how to analyze the results. The following steps are but one way, as you get more comfortable with analyses, software and scripting you can try to explore other options.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-mOK81uV97H0/Vhv0jwxvYuI/AAAAAAAADc0/koeqhFnki2w/s1600/mean%2Bdiffeo%2BFA.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="195" src="https://1.bp.blogspot.com/-mOK81uV97H0/Vhv0jwxvYuI/AAAAAAAADc0/koeqhFnki2w/s640/mean%2Bdiffeo%2BFA.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example of normalized average FA of population of images</td></tr>
</tbody></table>
</div>
<div>
</div>
</div>
<div style="text-align: justify;">
<div class="separator" style="clear: both; text-align: center;">
</div>
After normalization you can register your population template to MNI space. For this step you can for example use one of the provided atlases in FSL, like the ICBM152, which is an average of 152 T1-weighted MRI scans, linearly and non-linearly (6 iterations) transformed to form a symmetric model (<a href="http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin6" target="_blank">source</a>). The template is shown below.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin6" style="margin-left: auto; margin-right: auto;" target="_blank"><img border="0" height="400" src="https://1.bp.blogspot.com/-UU9KpRgci74/VhmgavPx75I/AAAAAAAADcQ/oE6LsSTX1cI/s400/mni_icbm152_nl_VI.jpg" width="286" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">MNI ICBM152 non-linear 6th generation. Image by FSL.</td></tr>
</tbody></table>
Registration software is notoriously bad at aligning images across modalities (like registering FA to T1). In this case, after many trial and errors, I have found that using asvDSM from DTI-TK has been best able to register FA files to T1 space, as you can see in the image below.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-9OxfmZkvHSY/Vi_MxNgHPLI/AAAAAAAADgI/_2FFORSAayU/s1600/ezgif.com-crop-5.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="370" src="https://3.bp.blogspot.com/-9OxfmZkvHSY/Vi_MxNgHPLI/AAAAAAAADgI/_2FFORSAayU/s640/ezgif.com-crop-5.gif" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Template FA (red) after registration to MNI space (gray-scale)</td></tr>
</tbody></table>
<div class="separator" style="clear: both; text-align: center;">
</div>
<b>asvDSM</b> by DTI-TK</div>
<div style="text-align: justify;">
<a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php" target="_blank">http://dti-tk.sourceforge.net/pmwiki/pmwiki.php</a></div>
<div>
<br />
Example script:<br />
<pre class="prettyprint linenums lang-bah">echo ~~~Register population template to MNI~~~
TVtool -in ${population_tensor_mean} -tr -out my_tr.nii.gz
BinaryThresholdImageFilter my_tr.nii.gz my_mask.nii.gz 0.7 100 1 0
TVtool -in ${population_tensor_mean} -mask my_mask.nii.gz -out ${population_tensor_mean}_strip.nii.gz
TVtool -in ${population_tensor_mean}_strip.nii.gz -fa -out ${population_tensor_mean}_strip_fa.nii.gz
asvDSM -template ${MNI_T1} -subject ${population_tensor_mean}_strip_fa.nii.gz -outTrans ${dtiToT1Trans} -sep 0.5 0.5 0.5 -ftol 0.0001
echo ~~~Run subject specific loop to warp to T1~~~
for i in `cat subjects.txt`;
do
echo ~Compose full warp and apply~
dfRightComposeAffine -aff ${subj}_affineTrans -df ${subj}_diffeoTrans -out ${subj}_combined.df.nii.gz
echo ~Combine full warp with affine to T1 and apply~
dfLeftComposeAffine -df ${subj}_combined.df.nii.gz -aff ${dtiToT1Trans} -out ${subj}_toT1_combined.df.nii.gz
deformationSymTensor3DVolume -in ${subj}_orig -trans ${subj}_toT1_combined.df.nii.gz -target ${T1} -out ${subj}_tensorFile
done
</pre>
<div style="text-align: justify;">
<i>
Q5: Open fslview, click "File" -> "Open standard". In this directory and in the directory atlases you can browse all the different atlases that are automatically loaded with FSL. You can also find the atlas images in your installed FSL directory. Find the MNI template that you would like to use for registration. What is the name of that file? Give 1 reason why you would want to register your data to this template.</i></div>
<br />
<h4>
Additional online resources:</h4>
- More on the available <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases" target="_blank">atlases in FSL</a><br />
- More on the <a href="http://www.bic.mni.mcgill.ca/ServicesAtlases/HomePage" target="_blank">atlases by MNI</a><br />
- <a href="http://www.dtiatlas.org/" target="_blank">White matter atlas</a> by Hermoye et al.<br />
- International consortium of brain mapping (ICBM) <a href="http://www.loni.usc.edu/ICBM/Downloads/Downloads_DTI-81.shtml" target="_blank">DTI-81 atlas</a><br />
- Cortical association tracts by <a href="http://onlinelibrary.wiley.com/doi/10.1002/mrm.10074/abstract;jsessionid=920AF48A97A8CA271C0901C7FB388955.f04t02" target="_blank">Mori et al. (2002)</a><br />
- Fiber tract based atlas of human white matter by <a href="http://pubs.rsna.org/doi/pdf/10.1148/radiol.2301021640" target="_blank">Wakana et al. (2004)</a><br />
- A diffusion tensor tractography atlas by <a href="http://www.sciencedirect.com/science/article/pii/S0010945208001238" target="_blank">Catani & De Schotten (2008)</a><br />
- More online <a href="http://cmrm.med.jhmi.edu/cmrm/atlas/human_data/file/JHUtemplate_newuser.html" target="_blank">atlases by JHU</a><br />
<br />
<h2>
4. Make scalars and check quality</h2>
<div style="text-align: justify;">
As a next step we will make diffusion maps (FA, MD, AD, RD) of each individual in template space. These diffusion maps are also known as <a href="https://en.wikipedia.org/wiki/Scalar_(mathematics)" target="_blank">scalar</a> images; they have magnitude but not direction. When you have those images you can go through them and get an impression of the quality of the normalization and your data. One way of quickly viewing all your images is by concatenating them into one 4 dimensional image using fslmerge.<br />
<br />
<b>fslmerge</b> by FSL<br />
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils" target="_blank">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils</a><br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td><a href="http://4.bp.blogspot.com/-QfiJ99vztLc/VhvzFrBtL3I/AAAAAAAADco/uCxpu-GSSbo/s1600/Normalized_FA.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="371" src="https://4.bp.blogspot.com/-QfiJ99vztLc/VhvzFrBtL3I/AAAAAAAADco/uCxpu-GSSbo/s640/Normalized_FA.gif" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="font-size: 13px;">Movie of normalized FA images</td></tr>
</tbody></table>
</div>
Example script:<br />
<pre class="prettyprint linenums lang-bsh">echo ~~~Produce subject specific smoothed scalars~~~
for subj in `cat subjects.txt`;
do
echo ~Produce diffusion maps~
TVtool -in ${subj}_tensorFile.nii.gz -fa -out ${subj}_fa.nii.gz
TVtool -in ${subj}_tensorFile.nii.gz -tr -out ${subj}_tr.nii.gz
TVtool -in ${subj}_tensorFile.nii.gz -ad -out ${subj}_ad.nii.gz
TVtool -in ${subj}_tensorFile.nii.gz -rd -out ${subj}_rd.nii.gz
echo ~Smooth diffusion maps~
SVGaussianSmoothing -in ${subj}_fa.nii.gz -fwhm 4 4 4 -out ${subj}_fa4mm.nii.gz
SVGaussianSmoothing -in ${subj}_tr.nii.gz -fwhm 4 4 4 -out ${subj}_tr4mm.nii.gz
SVGaussianSmoothing -in ${subj}_ad.nii.gz -fwhm 4 4 4 -out ${subj}_ad4mm.nii.gz
SVGaussianSmoothing -in ${subj}_rd.nii.gz -fwhm 4 4 4 -out ${subj}_rd4mm.nii.gz
done
echo ~~~Merge FA images for quality control~~~
fslmerge -t all_images_MNI_FA *_fa.nii.gz
</pre>
<div style="text-align: justify;">
<i>Q6: Some of the code in this tutorial uses a "for loop" for the first time. Take a look at a tutorial on loops <a href="http://ryanstutorials.net/bash-scripting-tutorial/bash-loops.php" target="_blank">here</a>. Try to figure out how "for loops" work, then describe what you would put in the subjects.txt file from line 2. HINT: Remember how <a href="http://ryanstutorials.net/bash-scripting-tutorial/bash-variables.php" target="_blank">variables work in bash</a>. Another option is to run the below code, instead of line 2 and 3. Can you describe what this would do? </i><br />
<pre class="prettyprint linenums lang-bsh">for i in `ls *_diffeo_fa.nii.gz`;
do
subj=`echo $i | awk 'BEGIN{FS="_diffeo_fa.nii.gz"}{print $1}'`</pre>
<i>Q7: Note that DTI-TK outputs the trace (TR) instead of the MD value. Look up how trace relates to MD <a href="http://www.diffusion-imaging.com/2015/10/what-is-diffusion-tensor.html" target="_blank">here</a>, and write the fslmaths code to convert TR to MD. If you are unsure how to do this, type fslmaths in your command line to see all the help options. </i><br />
<i><br /></i>
<i>Q8: Next describe what lines 10-13 accomplish, if you are unsure read <a href="http://support.brainvoyager.com/functional-analysis-preparation/27-pre-processing/279-spatial-smoothing-in-preparation.html" target="_blank">this documentation</a> on spatial smoothing. Finally in line 16 we merge all the individual FA images, describe why you might want to do that.</i><br />
<i><br /></i></div>
<h4>
Additional online resources:</h4>
- <a href="https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Smoothing" target="_blank">More info</a> on smoothing your data<br />
- How to convert <a href="http://brainder.org/2011/08/20/gaussian-kernels-convert-fwhm-to-sigma/" target="_blank">FWHM to sigma</a><br />
<br />
<h2>
5. Run whole brain voxel-wise statistics</h2>
<div style="text-align: justify;">
Now that we have scalar images in normalized space for each individual we can run voxel-wise analyses. There is a plethora of tools that you can use to run your preferred statistical model. A method that is often used in diffusion imaging is tract-based spatial statistics (<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS/UserGuide" target="_blank">TBSS</a>) by FSL. Although this method has become the somewhat accidental standard, it has recently come under some scrutiny. <a href="http://www.sciencedirect.com/science/article/pii/S1053811914004984" target="_blank">Bach <i>et al.</i> (2014)</a> describe some important methodological considerations for using TBSS. And <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137565/" target="_blank">Swartz <i>et al.</i> (2014)</a> describe how TBSS's skeleton projection step actually reduces algorithm accuracy. This is due to developments in registration tools (e.g. tensor normalization with DTI-TK, and improved algorithms of advanced normalization tools [<a href="http://stnava.github.io/ANTs/" target="_blank">ANTS</a>]), which has made the methods used in TBSS obsolete. Furthermore, there is the possibility that you miss subtle effects that are in one of the smaller tracts, that may get removed when the data is skeletonized.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://www.amepc.org/qims/article/view/1315/1771" style="margin-left: auto; margin-right: auto;" target="_blank"><img border="0" height="191" src="https://3.bp.blogspot.com/-yl3TzZevfUI/VhwOnffn5TI/AAAAAAAADdQ/MGr4twzv2EA/s320/1315-PB13-R1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example of tract-based spatial statistics. Image by FSL</td></tr>
</tbody></table>
Due to these issues, I have a preference for running whole brain voxel-wise analyses. In this case I decided to use FSL's randomise, since it is free and does not depend on software that has to run in MatLab (like <a href="http://www.fil.ion.ucl.ac.uk/spm/" target="_blank">SPM</a> and <a href="http://www.math.mcgill.ca/keith/surfstat/" target="_blank">SurfStat</a>). Randomise runs a non-parametric test, which means that it makes no assumptions about the underlying normality of the data, which is a more robust test than ordinary GLM's. If you read the randomise documentation you will also see that you can include covariates like age and sex. Which are important variables to include in anatomical studies.</div>
<br />
<b>randomise</b> by FSL<br />
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide" target="_blank">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide</a><br />
<br />
Example code:<br />
<pre class="prettyprint linenums lang-bsh">randomise -i all_merge_fa.nii.gz -o randomise_out -d test.mat -t test.con -m my_mask.nii.gz -T
</pre>
<br />
<div style="text-align: justify;">
<i>Q9: Look at the user guide for randomise, and try to get an impression of what the .mat file and .con file contain. Produce an example of both for the imaginary case that you have a patient group (n=3) and healthy control group (n=3), and you want to test where FA is significantly lower in the patient group, and where FA is significantly lower in the control group. What does the option -T stand for in randomise?</i><br />
<i><br /></i>
<i>Q10: Open fslview and open a standard 1mm MNI T1 image. Download example t-stat map (test_tstat1.nii.gz <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">here</a>), and add it to the MNI image. What you see is not yet very informative, click on the information dialog button (i in blue circle) and change the Lookup table options. Make the first Red-Yellow, and turn the second one on, and make it Blue-Lightblue. Close the dialog screen. Go to <a href="http://www.graphpad.com/quickcalcs/statratio1/" target="_blank">this p-value calculator</a>, and input in the t-ratio boxes that you want a probability that is 0.05 and you have 5 degrees of freedom (number of subjects - 1). What is your 2-tailed significant t-statistic? Input that number in the fslview screen as a minimum brightness value, and make 5 your maximum brightness value. Although this is obviously just test data, you now see what a noisy (when you only test 6 subjects you will most likely only find noise) t-stat map can look like. It is now up to you to start using your own data and improve on this! Remember to correct for multiple comparison when running your data to avoid inflated false discovery rates. You are running a t-tests on each individual voxel, which combine to multiple thousands per brain, and you will have to correct for this. Read more about familywise error rates <a href="https://en.wikipedia.org/wiki/Familywise_error_rate" target="_blank">here</a>. What does the -T option do to help with this issue? HINT: Read <a href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr08ss1/tr08ss1.pdf" target="_blank">this .pdf on the background of threshold-free cluster enhancement (TFCE</a>).</i></div>
<br />
<h4>
Additional online resources:</h4>
<div>
- <a href="https://www.youtube.com/channel/UCZ7gF0zm35FwrFpDND6DWeA" target="_blank">YouTube channel on analysis of neuroimaging data</a> by Jeanette Mumford- <a href="http://www.bi.wisc.edu/~fox//2013/05/29/including-voxelwise-covariates-with-multistatic/" target="_blank">Tutorial</a> on including voxel-wise covariates using SurfStat</div>
<div>
- <a href="http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/Tensor_Statistics" target="_blank">Stanford university information</a> on voxel-wise tensor statistics and <a href="http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/DTI#Whole-brain_.28group.29_diffusion_statistics_using_SPM_or_TBSS" target="_blank">whole brain diffusion stats</a><br />
- Wikipedia on <a href="https://en.wikipedia.org/wiki/False_discovery_rate" target="_blank">false discovery rate (FDR)</a><br />
- <a href="http://www.stat.columbia.edu/~martin/Tools/Lec8-FunctionalConnectivity.pdf" target="_blank">Lecture</a> on Multiple comparison issues from Columbia University<br />
- <a href="http://www.fil.ion.ucl.ac.uk/spm/doc/papers/GLN.pdf" target="_blank">Genovese et al. (2002)</a> on FDR for neuroimaging<br />
- Brainvoyager software on <a href="http://support.brainvoyager.com/functional-analysis-statistics/40-cluster-tresholding/87-users-guide-the-multiple-comparisons-problem.html" target="_blank">multiple comparison correction</a> and <a href="http://support.brainvoyager.com/functional-analysis-statistics/40-cluster-tresholding/175-false-discovery-rate-fdr.html" target="_blank">FDR</a><br />
- <a href="http://www.math.mcgill.ca/keith/fmristat/#thresholding" target="_blank">More info on thresholding</a> by the late Keith Worthsley, and the seminal publication on significance testing in MRI by <a href="http://www.fil.ion.ucl.ac.uk/~karl/A%20unified%20statistical%20approach%20for%20determining%20significant%20signals.pdf" target="_blank">Worthsley et al. (1996)</a><br />
<br /></div>
<h2>
6. Share your data</h2>
<div style="text-align: justify;">
When you are done with your analyses and want to share your statistical maps publicly, you can use <a href="http://neurovault.org/" target="_blank">NeuroVault</a>. Sharing your statistical maps will help improve meta analyses, collaborations and science in general.</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://neurovault.org/" style="margin-left: auto; margin-right: auto;" target="_blank"><img border="0" height="216" src="https://3.bp.blogspot.com/-eUbIJc4Tp-A/VhwW4905wbI/AAAAAAAADdk/W-bTbZvkq6A/s400/Screen%2BShot%2B2015-10-12%2Bat%2B3.10.56%2BPM.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Upload your unthresholded statistical map here, and share your URL in your publication</td></tr>
</tbody></table>
<br />
<h4>
</h4>
<h4>
Answers:</h4>
<a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/DTI_Tutorial_2_answers.pdf" target="_blank">DTI Tutorial 2 Answers</a></div>
<div style="text-align: justify;">
<br />
<span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">---------------------</span><br />
Reference this post as:<span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;"> </span><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">Do Tromp, DTI Tutorial 2 - Normalization and Statistics, </span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; vertical-align: baseline;">The Winnower </em><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">3:e145653.31599 (2016). DOI: </span><a href="https://dx.doi.org/10.15200/winn.145653.31599" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; text-decoration: none; vertical-align: baseline;">10.15200/winn.145653.31599</a></div>
</div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-80832681902706423532015-10-08T23:58:00.001-05:002015-11-09T14:01:31.875-06:00The diffusion tensor, and its relation to FA, MD, AD and RD<div class="separator" style="clear: both; text-align: justify;">
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In <a href="http://www.diffusion-imaging.com/2009/05/diffusion-tensor-imaging-101.html" target="_blank">DTI 101</a> we described how diffusion tensor imaging estimates brain microstructure by modeling diffusion of water in the brain. We briefly discuss that this is done by using ellipsoid or ball shaped tensors. This leads to the question; what is a tensor? </div>
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<br /></div>
<blockquote class="tr_bq">
"Tensors are simply mathematical objects that can be used to describe physical properties, just like scalars and vectors." - <a href="http://www.doitpoms.ac.uk/tlplib/tensors/what_is_tensor.php" target="_blank">University of Cambridge</a></blockquote>
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Or, more specifically as described by <a href="https://www.blogger.com/href=%22https://www.khanacademy.org/video/linear-algebra-introduction-to-eigenvalues-and-eigenvectors" target="_blank">Kahn Academy</a>:</div>
<div class="separator" style="clear: both; text-align: center;">
<iframe allowfullscreen="" frameborder="0" height="355" mozallowfullscreen="" scrolling="no" src="https://www.khanacademy.org/embed_video?v=PhfbEr2btGQ" webkitallowfullscreen="" width="560"></iframe>
</div>
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In the scanner the diffusion tensor is measured by imaging the diffusion in individual gradient directions, like in the image below. If you are interested a more in depth mathematical explanation of this principle is provided in our post <a href="http://www.diffusion-imaging.com/2014/04/from-diffusion-weighted-images-to.html" target="_blank">here</a>.</div>
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<br /></div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-G0PGY7-ElV8/Vhc9Da90eHI/AAAAAAAADaE/Dn4MZC8hQBE/s1600/Tensor%2Bexplained.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="174" src="http://3.bp.blogspot.com/-G0PGY7-ElV8/Vhc9Da90eHI/AAAAAAAADaE/Dn4MZC8hQBE/s640/Tensor%2Bexplained.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example work out for 6 gradient directions, from diffusion image to tensor</td></tr>
</tbody></table>
<div class="separator" style="clear: both; text-align: justify;">
Once the water diffusion is measured there are a number of ways you can quantify the shape of the tensors in each voxel. In DTI there are 4 measures that are most commonly used; fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity. These measure directly relate to the value of the three main eigenvalues of the tensor, indicated in the figure below with <span style="color: red;">lambda 1</span>, <span style="color: lime;">lambda 2</span> and <span style="color: blue;">lambda 3</span>. What is an eigenvalue of a tensor? It is the value of the displacement/diffusion for each specific vector. Or, as defined by wikipedia: </div>
<br />
<blockquote class="tr_bq">
"If a two-dimensional space is visualized as a rubber sheet, a linear map with two eigenvectors and associated eigenvalues λ1 and λ2 may be envisioned as stretching/compressing the sheet simultaneously along the two directions of the eigenvectors with the factors given by the eigenvalues." - <a href="https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors#Overview" target="_blank">Wikipedia</a></blockquote>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-yGDvpFMp55E/VhdDU8noMnI/AAAAAAAADac/sQwoak3o5pM/s1600/Eigenvectors.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://4.bp.blogspot.com/-yGDvpFMp55E/VhdDU8noMnI/AAAAAAAADac/sQwoak3o5pM/s1600/Eigenvectors.gif" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example of the influence of the transformation (or in our case diffusion) on the eigenvalues </td></tr>
</tbody></table>
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There are roughly two archetypical examples of diffusion tensors. On the left you see the example where the tensor has roughly equal eigenvalues for each main vector, thus showing an isotropic diffusion profile. On the right an example of diffusion primarily in one direction, thus demonstrating an anisotropic diffusion profile. </div>
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</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td><a href="http://3.bp.blogspot.com/-uH2o8Mw5gYQ/VhaCOM8QjJI/AAAAAAAADZA/tTVKCXP25Y8/s1600/Screen%2BShot%2B2015-10-08%2Bat%2B9.47.37%2BAM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="236" src="http://3.bp.blogspot.com/-uH2o8Mw5gYQ/VhaCOM8QjJI/AAAAAAAADZA/tTVKCXP25Y8/s400/Screen%2BShot%2B2015-10-08%2Bat%2B9.47.37%2BAM.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="font-size: 13px;">Examples of tensor shapes</td></tr>
</tbody></table>
<div class="separator" style="clear: both; text-align: justify;">
Characterization of each eigenvalue in the tensor and combinations of them help constitute the main diffusion measures. Specifically, in axial diffusivity (AD) we only quantify the value of <span style="color: red;">lambda 1</span>. While in radial diffusivity (RD) we take the average of <span style="color: lime;">lambda 2</span> and <span style="color: blue;">lambda 3. </span>Mean diffusivity (MD) provides an average of all three, <span style="color: red;">lambda 1</span>, <span style="color: lime;">lambda 2</span>, and <span style="color: blue;">lambda 3</span>, this measure is sometimes also referred to as trace (TR), which is the sum of <span style="color: red;">lambda 1</span>, <span style="color: lime;">lambda 2</span> and <span style="color: blue;">lambda 3</span>. And finally fractional anisotropy (FA) provides the relative difference between the largest eigenvalue as compared to the others; it quantifies the fraction of diffusion that is anisotropic. The exact mathematical relations are shown below:</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td><a href="http://1.bp.blogspot.com/-2-X4yw26I18/VhaCOCL8e0I/AAAAAAAADY8/ccLKAAUS9u0/s1600/Screen%2BShot%2B2015-10-08%2Bat%2B9.47.55%2BAM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="126" src="http://1.bp.blogspot.com/-2-X4yw26I18/VhaCOCL8e0I/AAAAAAAADY8/ccLKAAUS9u0/s400/Screen%2BShot%2B2015-10-08%2Bat%2B9.47.55%2BAM.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="font-size: 13px;">Relation of diffusivity measures to the eigenvalues of the tensor<br />
<div>
<br /></div>
</td></tr>
</tbody></table>
<div style="text-align: justify;">
When you plot the value of each a diffusivity measure in every voxel you can get scalar maps of the quantification of the local tensors. In the image below you see representative examples of each measure. When you look at the MD map it becomes apparent that the mean diffusivity measure is specifically sensitive to cerebral spinal fluid (CSF), which has high values of average diffusion. In voxels with much CSF mean diffusivity is high, and values are thus bright. While AD is only sensitive to diffusion in the longest eigenvalue. Here you see that highly organized structures like white matter pathways are brights. In addition large open cavities like ventricles have general high levels of diffusion which translates to high <span style="color: red;">lambda 1</span> values. Then RD represents the two shortest eigenvalues and shows dark values in highly organized and dense structures like white matter pathways, intermediate values in gray matter, and high values in regions with CSF. Finally, FA plots the relative length of <span style="color: red;">lambda 1</span>, compared to <span style="color: lime;">lambda 2</span> and <span style="color: blue;">lambda 3</span>. This leads to selective brightness in white matter, but not gray matter or CSF.</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://2.bp.blogspot.com/-Dyl5V0rBV2U/VQnVvNvmHGI/AAAAAAAACwg/JBolWC1qdlw/s1600/scalars.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="640" src="http://2.bp.blogspot.com/-Dyl5V0rBV2U/VQnVvNvmHGI/AAAAAAAACwg/JBolWC1qdlw/s640/scalars.png" width="396" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Examples of each diffusivity measure, FA, MD, AD and RD</td></tr>
</tbody></table>
Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-10396338568566545912015-10-04T19:55:00.000-05:002018-01-10T13:46:51.446-06:00DTI Tutorial 1 - From Scanner to Tensor<div style="font-size: 100%; text-align: justify;">
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<a href="http://3.bp.blogspot.com/-G-DH_fB_VJM/Vgqo5QRgt1I/AAAAAAAADQc/_6gA2qE4G4A/s1600/tutorial.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://3.bp.blogspot.com/-G-DH_fB_VJM/Vgqo5QRgt1I/AAAAAAAADQc/_6gA2qE4G4A/s640/tutorial.jpg" width="640" /></a></div>
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Starting out with data analysis often seems like a daunting task, as there are innumerable software packages with sometimes poor documentation. To make this process easier and help you get started with diffusion imaging analyses I put together this tutorial that will introduce you to the most important processing steps and tools. Always keep in mind that there are many ways of processing your data, and the examples used in this tutorial provide just one way of doing things. As you get more versed with the different steps and software you will be able to try different packages and see which you prefer best.<br />
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There will be questions in each section to allow you to actively work with data and tools. These question will sometimes ask you to install software or download example data, you can find the <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">example data for this tutorial here</a>. Most of the publicly available (and thus free) software tools used in neuro-imaging will only work in a bash terminal environment (linux or mac). If you are not already, try to become comfortable in a linux environment. An example of linux tutorials can be found online, for example the "<a href="http://www.ee.surrey.ac.uk/Teaching/Unix/" target="_blank">UNIX Tutorial for Beginners</a>" contains explanations of basic commands commonly used to navigate the terminal. If you would like some advanced tutorials that go into scripting in bash you can check out <a href="http://tldp.org/HOWTO/Bash-Prog-Intro-HOWTO.html" target="_blank">this linux tutorial</a>, as scripting in linux will become an important skill when datasets get large. Finally, this DTI tutorial assumes a basic understanding of diffusion MRI, if you would like to catch up on that first, you can go trough <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/DTI_Tutorial_1_background.pdf" target="_blank">these</a> slides that introduce this tutorial. Beyond that you can check out the post on <a href="http://www.diffusion-imaging.com/2009/05/diffusion-tensor-imaging-101.html" target="_blank">diffusion imaging 101</a>, and start perusing other posts on this website.<br />
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In this first tutorial the overall goal is to demonstrate how to convert raw diffusion data files to tensor images. In order to get to tensor images there are a number of intermediate steps that are essential to go through:<br />
1. You will have to move the data off the scanner and convert it to a usable format.<br />
2 & 3. It is important to deal with distortions common to diffusion images, like EPI and eddy currents. <br />
4. Make sure you remove any non-brain tissues.<br />
5. Make the gradient directions file.<br />
6. Fit the tensors.<br />
7. Check the fit of the tensors.<br />
At the end of the document you will find a pdf with the answers to the questions in this tutorial, although try to figure things out yourself as much as possible. If you run into issues going through this tutorial please direct your questions to the <a href="http://www.diffusion-imaging.com/2013/03/diffusion-imaging-discussion-forum.html" target="_blank">diffusion imaging forum</a>, where there will likely be someone able to help you out. Please make sure to describe your question as clearly as possible. Remember to please direct software specific questions to their own respective forums or mail lists. </div>
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<h2>
1. From scanner to NifTI</h2>
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What is the best way to get your scanner data to a format that you can actually look at? It depends on your preferences and your scanner. GE and Siemens scanners output DICOM (which stands for Digital Imaging and COmmunications in Medicine) format. While Philips scanners output PAR/REC format. While it is possible to view DICOM or PAR/REC images using specialized software, they are not suitable for processing. Formats that are more commonly used for processing and viewing is the NifTI format. NifTI stands for Neuroimaging informatics Technology Initiative, started by NIH to help streamline the available tools for neuroimaging research. To convert DICOM or PAR/REC images to NifTI you can use software tools like dcm2nii or others. </div>
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<b>dcm2nii</b> by <span style="background-color: white;">MRIcron</span></div>
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<a href="http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html">http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html</a></div>
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<i>Q1: In your document write the command code to how show how you would convert your raw scanner image to NifTI, assume your data is in dicom format, and organized like this:</i><br />
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<a href="http://1.bp.blogspot.com/-PBn0HvZ2g6s/VhG5HD6YW8I/AAAAAAAADV4/fGTbbvYiu5U/s1600/Screen%2BShot%2B2015-09-29%2Bat%2B3.19.30%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="64" src="https://1.bp.blogspot.com/-PBn0HvZ2g6s/VhG5HD6YW8I/AAAAAAAADV4/fGTbbvYiu5U/s640/Screen%2BShot%2B2015-09-29%2Bat%2B3.19.30%2BPM.png" width="640" /></a></div>
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In order to get an impression of the quality of the data that you got off the scanner it will be important to take a closer look at your data. This will be a recurring activity, as you can only really make sure your data is valid and your analysis steps work by looking at your data. An example of a toolbox that can help you with that was developed by <span style="text-align: justify;">the University of Oxford FMRIB software library (</span><a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/" style="text-align: justify;" target="_blank">FSL</a><span style="text-align: justify;">)</span>. A good way to get familiar with some of their software tools is to go through the <a href="http://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/" target="_blank">FSL tutorial on their website</a>.<br />
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One method to investigate the information of your image is to look at the header information. A tool that can help you with that is fslinfo. The image below explains the information that you get when you use fslinfo on a diffusion image. The numbers in blue indicate the image dimensions (<span style="color: blue;">256x256x67</span>), in red you see the total number of volumes (<span style="color: red;">49</span>), and in green the voxel dimensions (<span style="color: lime;">1x1x2mm</span>). Another good habit is to view your data after it has been collected. This way you can catch any issues that may have occurred during the scan session. More info on why and how to do this can be found <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-1-acquisition.html" target="_blank">here</a>. A commonly used image visualization tool is fslview.</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="-webkit-text-stroke-width: 0px; font-family: -webkit-standard; letter-spacing: normal; margin-bottom: 0.5em; margin-left: auto; margin-right: auto; orphans: auto; padding: 6px; text-align: center; text-indent: 0px; text-transform: none; widows: auto; word-spacing: 0px;"><tbody>
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<a href="http://3.bp.blogspot.com/-hUiCjTQichQ/UjHH66680MI/AAAAAAAABf8/VNCMmOj_3aQ/s1600/scan_info.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="255" src="https://3.bp.blogspot.com/-hUiCjTQichQ/UjHH66680MI/AAAAAAAABf8/VNCMmOj_3aQ/s400/scan_info.png" style="cursor: move;" width="400" /></a></div>
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Image information of the sample data</div>
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<b>fslinfo</b> by FSL<span style="text-align: start;"> (download FSL </span><a href="http://fsl.fmrib.ox.ac.uk/fsldownloads/" style="text-align: start;" target="_blank">here</a><span style="text-align: start;">)</span></div>
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<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils</a><br />
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<b>fslview</b> by FSL<br />
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslview/">http://fsl.fmrib.ox.ac.uk/fsl/fslview/</a><br />
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<i>Q2: Download our example diffusion weighted image (</i><i>052212_s09_dti.nii) </i><i>through <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">this link</a>, or use your own data. Open the image in fslview (if you don't know how, look at the fslview documentation, or type fslview in your terminal after installing the software). Scroll through the different volumes. Can you identify how many volumes were acquired without diffusion weighting (b=0)?</i></div>
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<h4>
Additional online resources:</h4>
- A wiki that provides more information on <a href="http://wiki.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI" target="_blank">converting diffusion images from the DICOM format</a><br />
- Video on how to <a href="https://www.youtube.com/watch?v=rv8pAIaRS9Q" target="_blank">use the graphical user interface of dcm2nii</a><br />
- Additional information from <a href="http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html" target="_blank">dcm2nii on data repositories and alternatives tools</a><br />
- Website dedicated to <a href="http://www.dclunie.com/medical-image-faq/html/index.html" target="_blank">medical imaging formats</a><br />
- Other dicom converters: <a href="https://surfer.nmr.mgh.harvard.edu/pub/docs/html/mri_convert.help.xml.html" target="_blank">mri_convert by freeserver</a>, <a href="http://afni.nimh.nih.gov/pub/dist/doc/program_help/Dimon.html" target="_blank">dimon by afni</a>, <a href="http://www.aimfeld.ch/neurotools/neurotools.html" target="_blank">dicom2nifti for SPM</a><br />
- Other data viewers: <a href="http://www.slicer.org/">3D Slicer: Image Visualization Software</a>, <a href="http://www-sop.inria.fr/asclepios/software/MedINRIA/index.php">MedINRIA: DTI Analysis and Visualization Software.</a><br />
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<h2>
2. Distortion correction (eddy)</h2>
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In the next step we will run an eddy distortion correction. <a href="https://en.wikipedia.org/wiki/Eddy_current" target="_blank">Eddy currents</a> are a natural effect of the changing magnetic field that are commonplace in diffusion scans. If you are interested you can find more background on eddy distortions in <a href="http://www.diffusion-imaging.com/2012/03/dti-preprocessing-distortion-correction.html" target="_blank">our post</a> on distortions corrections. The most common tool to counter eddy currents has been developed by FSL. In addition to correcting eddy currents this tool will correct for simple head motions. It takes each volume from your diffusion image and will apply a rigid registration (using FLIRT under the hood) to align all volumes to the first "reference" volume. It is common practice to set the reference volume to be the first volume that was acquired, thus number 0 (counting starts at 0). All the following volumes will then be registered to that first volume. </div>
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<b>eddy_correct</b> by FSL</div>
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<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Eddy_Current_Correction">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Eddy_Current_Correction</a></div>
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<i>Q3: Run the command line code for the eddy correction as shown below. What does the output to the terminal look like? </i><i>Does it take long to run? Why?</i> </div>
<pre class="prettyprint linenums lang-bsh">eddy_correct 052212_s09_dti.nii 052212_s09_dti_eddy.nii 0</pre>
(If you'd like to quit before its done; type ctrl-c in your terminal, the output files are available to download: <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/052212_s09_dti_eddy.ecclog">052212_s09_dti_eddy.ecclog</a> and <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/052212_s09_dti_eddy.nii">052212_s09_dti_eddy.nii</a>)<br />
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<h4>
Additional online resources:</h4>
<span style="text-align: start;">- You can also use the <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide" target="_blank">FSL Diffusion Toolbox</a>, which has a graphical user interface</span><br />
<span style="text-align: start;">- If you acquired two diffusion scans with </span>opposing polarity, you can use <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/EDDY/UsersGuide" target="_blank">FSL's eddy</a> instead<br />
- Distortion correction and preprocessing <a href="http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/Preprocessing_DTI_Data_Using_FSL" target="_blank">tutorial from Stanford</a> </div>
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<h2>
3. Distortion correction (EPI)</h2>
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EPI (aka field or B0-susceptibility) distortions are caused by magnetic field inhomogeneities, and are more obvious to spot then eddy currents. You can find more info on EPI distortions in <a href="http://www.diffusion-imaging.com/2012/03/dti-preprocessing-distortion-correction.html" target="_blank">this blog post</a>. An example of EPI distortions is shown in the image below. FSL has great tutorials and tools that help deal with EPI distortions using a field map that you should acquire in addition to your diffusion scan. The process essentially consists of two steps, first compose the field map from the raw phase and magnitude files (acquired as part of the field map sequence), and second apply the field map to your diffusion image. Field maps can be collected in different ways, you may have to talk to your local MRI-guru to figure out how your data is collected, but fear not -- if you instead collected two diffusion-scans with opposing polarity and no field map, you can use the TOPUP tool to combine both.<br />
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<b>FUGUE</b> by FSL<br />
<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide?highlight=%28%5CbCategoryFUGUE%5Cb%29">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide?highlight=%28%5CbCategoryFUGUE%5Cb%29</a><br />
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<b>TOPUP</b> by FSL</div>
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<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP</a></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-U0XKPqDBC5I/VgsH8yQPQdI/AAAAAAAADSU/NyiFB7S5bdk/s1600/Screen%2BShot%2B2015-09-29%2Bat%2B4.47.04%2BPM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="260" src="https://1.bp.blogspot.com/-U0XKPqDBC5I/VgsH8yQPQdI/AAAAAAAADSU/NyiFB7S5bdk/s400/Screen%2BShot%2B2015-09-29%2Bat%2B4.47.04%2BPM.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example of EPI distortions in the frontal lobe of <i style="text-align: justify;">052212_s09_dti.nii</i></td></tr>
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<i>Q4: On FSL's FUGUE website find the image that shows a real field map image. Can you get an impression from that image where you would expect most distortion to be localized? Look at the field map from the provided example data (download</i><i> 052212_s10_fmap.nii </i><i><a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">here</a>). Does that agree with the distortion that you see in the raw diffusion image </i><i>(052212_s09_dti.nii)</i><i>?</i><br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-qC_If8jkgow/VhCAj9JYZ9I/AAAAAAAADVk/sKM4_ZLYbcg/s1600/ezgif.com-crop.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="267" src="https://1.bp.blogspot.com/-qC_If8jkgow/VhCAj9JYZ9I/AAAAAAAADVk/sKM4_ZLYbcg/s400/ezgif.com-crop.gif" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Pre and post (green) EPI distortion correction</td></tr>
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<i>Q5: Note in the image above how the diffusion image is altered after the field map was applied (in green) and EPI distortion was corrected. Take a close look at the gif and indicate what brain regions show reductions in stretch distortion.</i><br />
<i><br /></i></div>
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<h4 style="text-align: start;">
Additional online resources:</h4>
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<span style="text-align: start;">- Distortion correction tutorial for TOPUP from <a href="http://fmri.ucsd.edu/Howto/3T/DTI-TOPUP.html" target="_blank">UCDavis</a></span><br />
- Distortion correction tutorial by <a href="http://andysbrainblog.blogspot.com/2014/08/dti-analysis-steps-1-2-distortion.html" target="_blank">Andy's Brain Blog</a>, with video's<br />
- EPI distortion correction using <a href="http://sites.cardiff.ac.uk/cubric/cubric-users/user-documentation/mri-resources/mri-how-to/effective-epi-distortion-correction-in-exploredti-v4-8-3/" target="_blank">ExploreDTI</a><br />
- Distortion correction tutorial by <a href="http://www.bic.mni.mcgill.ca/~thayashi/dti.html" target="_blank">MNI</a><br />
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</h3>
<h2>
4. Stripping the Skull </h2>
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In this step the objective is to isolate the brain and get rid of unwanted skull and tissue from the scan so that further processing occurs solely on brain tissue. FSLs automated brain extraction tool (bet) is a tool that can assist in that process. It is important to check if the output from the automated masking process was effective, if not, then you can alter the bet extraction options or manually edit the mask. It should be noted that bet is optimized for human brain extraction, and manual extraction if often necessary in other species. The image below shows different examples of brain extraction with bet, in red is an example of an output mask of the brain tissue.</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET" style="margin-left: auto; margin-right: auto;" target="_blank"><img alt=" FSL BET" border="0" height="211" src="https://1.bp.blogspot.com/-h1T5aQOstMo/Vg2X9pDjiAI/AAAAAAAADTY/Ra3rr8SIORo/s640/bet2_eg_small.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Brain extraction using FSL's bet. Image courtesy of <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET" target="_blank">FSL</a></td></tr>
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<b>bet</b> by fsl</div>
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<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET</a><br />
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<div style="text-align: justify;">
<i>Q6: Investigate the command options in bet using the help function or the online documentation. Explain what executing the below command would entail. How would you alter these options if your output mask is too small? Run a brain extraction on the sample data (e.g. </i><a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/052212_s09_dti_eddy_fm.nii" style="text-align: start;">052212_s09_dti_eddy_fm.nii</a>)<i> using the code below, add options in the command so that it will output a binary mask, and so that it will run multiple iterations for a robust output.</i></div>
<pre class="prettyprint linenums lang-bsh">bet 052212_s09_dti_eddy_fm.nii 052212_s09_dti_eddy_fm_strip -f 0.3
</pre>
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<h4 style="text-align: start;">
Additional online resources:</h4>
<div style="text-align: start;">
<span style="text-align: start;">- <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/FAQ" target="_blank">BET</a> frequently asked questions</span><br />
<span style="text-align: start;">- Other skull stripping tools: <a href="http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dSkullStrip.html" target="_blank">3dSkullStrip by AFNI</a>, <a href="https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Skull_Stripping#SPM" target="_blank">SPM</a>, <a href="http://brainsuite.org/processing/surfaceextraction/bse/" target="_blank">BrainSuite</a></span><br />
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<h2>
5. Gradient directions</h2>
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The gradient information from the scanner is used in order to calculate the tensors models. More information on the mathematics can be found in our post <a href="http://www.diffusion-imaging.com/2014/04/from-diffusion-weighted-images-to.html" target="_blank">here</a>. The gradient information is sometimes stored in the header of the images, if it is not, talk to your scan tech to get this information. There are two aspects that have to be taken into account, the b-values and the b-vectors. Diffusion-weighted b-values usually range from 750~1500 s/m^2, and is most frequently set at 1000s/m^2. </div>
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<blockquote class="tr_bq">
“The b-value identifies the measurement’s sensitivity to diffusion and determines the strength and duration of the diffusion gradients.” (<a href="http://clinical-mri.com/wp-content/uploads/software_hardware_updates/Graessner.pdf" target="_blank">Graessner, 2011</a>). </blockquote>
Depending on the used b-value (in below example is: 1000s/m^2), and the number of b=0 volumes (in the example: 6) an abbreviated version of the b-value document can look like this:<br />
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<span style="background-color: black; color: white; font-family: "courier new" , "courier" , monospace; font-size: 13px; text-align: center;">0,0,0,0,0,0,1000,1000,1000,1000,1000,1000,1000,1000,1000,1000,1000</span></div>
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In contrast the b-vectors consist out of 3 separate vectors (x, y, z) for each gradient direction acquired. A file containing b-vectors might look like this:</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-MGAxpSHgzuI/UxajYlKfSDI/AAAAAAAABpw/hPkzXSPI7eo/s1600/Screen%2BShot%2B2014-03-04%2Bat%2B10.08.50%2BPM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://4.bp.blogspot.com/-MGAxpSHgzuI/UxajYlKfSDI/AAAAAAAABpw/hPkzXSPI7eo/s320/Screen%2BShot%2B2014-03-04%2Bat%2B10.08.50%2BPM.png" width="254" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Examples of b-vectors</td></tr>
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<div style="text-align: justify;">
The format used for the b-vectors and b-values depends on the final program that you are going to use to calculate the tensors. Since we are going to use <a href="http://cmic.cs.ucl.ac.uk/camino//index.php" target="_blank">Camino</a> software to run the tensor calculation in this tutorial, we will have to convert the b-values and b-vectors to a scheme file format that combines both, using fsl2scheme. The abbreviated scheme file will look the image below, where the first three columns indicate the b-vectors, and the 4th shows the b-values.</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://2.bp.blogspot.com/-Q1i4bKa0f0k/VhHGUW0pdVI/AAAAAAAADWE/vTm_YSoDrL8/s1600/SCHEME%2Bexample.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="225" src="https://2.bp.blogspot.com/-Q1i4bKa0f0k/VhHGUW0pdVI/AAAAAAAADWE/vTm_YSoDrL8/s400/SCHEME%2Bexample.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Example scheme file (left), diffusion weighted volumes (right)</td></tr>
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<div style="text-align: justify;">
<b>fsl2scheme</b> by Camino (download Camino <a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Main.Download" target="_blank">here</a>)</div>
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<a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Fsl2scheme">http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Fsl2scheme</a></div>
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<div>
<i>Q7: Investigate the documentation of fsl2scheme, provide example code of how to convert b-vectors and b-values from an FSL format to a Camino scheme file.</i></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
If you ran an eddy correction on you data you will first have to correct your scheme file. This is a necessary step as the rigid registration makes the volumes move, causing the gradient directions to be misaligned. Here are a couple of resources to explain this process further and provide code to run the correction:</div>
1. Blog on <a href="http://blog.cogneurostats.com/?p=302" target="_blank">rotating bvecs for DTI fitting</a><br />
<div>
2. <a href="http://onlinelibrary.wiley.com/doi/10.1002/mrm.21890/abstract" target="_blank">Publication</a> on why you should do this</div>
3. <a href="https://github.com/bernardng/codeSync/blob/master/dMRIanalysis/rotatebvecs" target="_blank">Github code</a> on how to run this rotation<br />
<br />
<b>rotate_bvectors.sh</b> by bernardng<br />
<a href="https://github.com/bernardng/codeSync/blob/master/dMRIanalysis/preprocess_batch.sh">https://github.com/bernardng/codeSync/blob/master/dMRIanalysis/preprocess_batch.sh</a></div>
<div>
<br />
Combined example code to rotate and produce scheme file:</div>
<div>
<pre class="prettyprint linenums lang-bsh">prefix=052212_s09
sh rotate_bvectors.sh bvecs.txt ${prefix}.bvecs EDDY/
rm -f ${prefix}*.mat
fsl2scheme -bvecfile ${prefix}.bvecs -bvalfile bval.txt > ${prefix}.scheme
</pre>
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<br />
<h4 style="text-align: start;">
Additional online resources:</h4>
<div style="text-align: start;">
<div style="text-align: start;">
- <a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Docs.SchemeFiles" target="_blank">Camino website</a> with more details on the scheme file formats and application<br />
- <a href="https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3c23f260.0903" style="text-align: start;" target="_blank">FSL forum</a><span style="text-align: start;"> with more details and code for </span>b-vector rotation </div>
<span style="text-align: start;"><br /></span></div>
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<h2>
6. Tensor Fitting</h2>
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In this step we will use <a href="http://cmic.cs.ucl.ac.uk/camino/" target="_blank">Camino</a> to run the tensor calculation. This tool is currently my preferred software for this step as it regularly implements novel mathematical models and scientific developments in diffusion MRI. In order to use this tool, the NifTI format (.nii) images will have to be converted to Camino format (.Bfloat). To do this you can use Camino's tool image2voxel. Once the data is in .Bfloat format you can use Camino to run the tensor model estimation from the diffusion weighted images, using modelfit. Once this is done, you will have to convert the data back to .nii format, using dt2nii. Which will read the header information from a reference NifTI file and copy that to the new tensor NifTI file.</div>
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<b>image2voxel</b> by Camino</div>
<a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Image2voxel">http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Image2voxel</a><br />
<br />
<b>modelfit</b> by Camino<br />
<a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Modelfit">http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Modelfit</a><br />
<br />
<b>dt2nii</b> by Camino<br />
<a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Dt2nii">http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Dt2nii</a><br />
<br />
Example code:<br />
<pre class="prettyprint linenums lang-bsh">prefix=052212_s09
export CAMINO_HEAP_SIZE=2000
image2voxel -4dimage ${prefix}_dti_eddy_fm.nii > ${prefix}_DWI.Bfloat
modelfit -inputfile ${prefix}_DWI.Bfloat -schemefile ${prefix}.scheme -outputdatatype float > ${prefix}_DTI.Bfloat
dt2nii -inputfile ${prefix}_DTI.Bfloat -inputdatatype float -header ${prefix}_dti_eddy_fm.nii -outputroot ${prefix}_dti_eddy_fm_strip_
</pre>
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<div>
<div style="text-align: justify;">
<i>Q8: Download the necessary data from our <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">example data</a>. Using the example code shown above try to run the tensor fitting. Note that ${prefix} indicates a variable in bash, which is defined in line 1, if you are unsure how variables work in bash look it up <a href="http://ryanstutorials.net/bash-scripting-tutorial/bash-variables.php" target="_blank">here</a>. Learning how to work with variables now will make it easier to transition to loops in scripts later on. This code will need a couple of minutes to run. Side note; to avoid a java error message (as shown below) we increased the java RAM usage allowed for Camino with the third line in the example script. Solution came from the <a href="http://www.nitrc.org/pipermail/camino-users/2012-March/000070.html" target="_blank">camino help list</a>.</i></div>
<pre class="prettyprint linenums lang-bsh">Exception in thread "main" java.lang.OutOfMemoryError: Java heap space</pre>
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<div style="text-align: justify;">
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<i>Q9: Imagine that due to unforeseen reasons you collected noisy data, and you would like to run a robust tensor estimation as described by <a href="http://onlinelibrary.wiley.com/doi/10.1002/mrm.20426/abstract" target="_blank">Chang, Jones & Pierpaoli (2005)</a>. Investigate the Camino <a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Main.ManPages" target="_blank">list of available commands</a>. What tool would be able to accommodate your aim?</i><br />
<br /></div>
<h4>
Additional online resources:
</h4>
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<div style="text-align: start;">
<span style="text-align: start;">- More info from </span><a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-2-tensor.html" target="_blank">this blog</a> on tensor fitting<br />
<span style="text-align: start;">- Camino has a comprehensive list of tutorials for their tools <a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Main.Tutorials" target="_blank">here</a></span><br />
<span style="text-align: start;">- <a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Tutorials.MultifibreReconstruction" target="_blank">Camino</a> can also run higher order models like Q-ball/HARDI</span></div>
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<h2>
7. Sanity Check</h2>
<div>
<div style="text-align: justify;">
Finally it will be essential to check if your scheme file was applied well, and your tensors were fit correctly. If you have not already go through the <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-2-tensor.html" target="_blank">tensor fitting quality control</a> post to see why and how. In order to see the tensor fit we will use tools by camino to draw and visualize the main tensor. In principle you should only have to check this for one sample image in your entire dataset.</div>
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<b>dteig</b> by Camino</div>
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<a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Man.Dteig">http://cmic.cs.ucl.ac.uk/camino/index.php?n=Man.Dteig</a></div>
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<b>pdview</b> by Camino</div>
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<div>
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<a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Man.Pdview">http://cmic.cs.ucl.ac.uk/camino/index.php?n=Man.Pdview</a></div>
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<br /></div>
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Example code:</div>
<pre class="prettyprint linenums lang-bah">prefix=052212_s09
dteig -inputmodel dt -inputdatatype float -outputdatatype float < ${prefix}_DTI.Bfloat > ${prefix}_DTI_EIG.Bfloat
pdview -inputdatatype float -inputmodel dteig -inputfile ${prefix}_DTI_EIG.Bfloat -datadims 256 256 67 &</pre>
<div style="text-align: justify;">
<i>Q10: Run the example code to investigate the tensor fit, compare the results to the examples from the <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-2-tensor.html" target="_blank">quality control post</a> and the <a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Tutorials.DTI#dt_fit" target="_blank">Camino website</a>, were the tensors fit correctly?</i><br />
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<div>
<h4>
Further additional online resources:</h4>
</div>
<div>
- General tutorial on "<a href="http://www.mccauslandcenter.sc.edu/CRNL/wp-content/tools/tutorial/html/dti.html" target="_blank">Diffusion Tensor Imaging Analysis using FSL</a>"<br />
- Overview of other software packages on <a href="http://www.diffusion-imaging.com/2013/03/list-of-software-tools-for-dti.html" target="_blank">this blog</a><br />
- <a href="http://www.sciencedirect.com/science/article/pii/S1053811912007306" target="_blank">Article</a> on the do's and don'ts of diffusion imaging<br />
- <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594764/" target="_blank">Article</a> that is the hitchhikers guide to diffusion imaging<br />
- Preprocessing tutorial by the <a href="http://enigma.ini.usc.edu/protocols/dti-protocols/" target="_blank">Enigma group</a><br />
<br />
<h4>
Answers:</h4>
</div>
<div>
<a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/DTI_Tutorial_1_answers.pdf" target="_blank">DTI Tutorial 1 Answers</a><br />
<br />
<br />
<span style="border: none; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; list-style: none; margin: 0px; outline: none; padding: 0px;">---------------------</span><br style="color: #333333; font-family: 'Open Sans'; font-size: 13px; text-align: justify;" /><span style="background-color: white; color: #333333; font-family: 'Open Sans'; font-size: 13px; text-align: justify;">Reference this post as:</span><span style="border: none; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; list-style: none; margin: 0px; outline: none; padding: 0px;"> </span><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px;">Do Tromp, DTI Tutorial 1 - From Scanner to Tensor, </span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">The Winnower </em><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px;">3:e145653.31559 (2016). DOI: </span><a href="https://dx.doi.org/10.15200/winn.145653.31559" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">10.15200/winn.145653.31559</a></div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-59202275109061834152015-03-25T12:40:00.001-05:002015-10-13T12:32:53.491-05:00Non-isotropic voxels; What to do and why<div class="separator" style="clear: both; text-align: center;">
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<a href="http://4.bp.blogspot.com/-DC5Xp26s7xQ/VRLzqraPsrI/AAAAAAAAC0o/-tQQcD5kaWY/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.42.34%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="132" src="http://4.bp.blogspot.com/-DC5Xp26s7xQ/VRLzqraPsrI/AAAAAAAAC0o/-tQQcD5kaWY/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.42.34%2BPM.png" width="320" /></a></div>
<br />
A <a href="https://groups.google.com/forum/#!topic/diffusion-imaging/gYAa6ak4G00" target="_blank">recent question came up on the forum</a> that was concerned with isotropic versus non-isotropic voxel sizes:<br />
<br />
<blockquote class="tr_bq">
Hi, I am trying to process some DTI data using FSL for TBSS or Voxel based comparison. I am following the basic guideline from <a href="http://www.cabiatl.com/Resources/Course/tutorial/html/dti.html" style="color: #1155cc;" target="_blank">http://www.cabiatl.com/<wbr></wbr>Resources/Course/tutorial/<wbr></wbr>html/dti.html</a> and this works good.<br />
<br />
However, I found my DTI data set is not isotrophic, about 0.9 x 0.9 x 3 mm.<br />
My question is<br />
<br />
<ol>
<li>Does the DTI voxel size matter for TBSS or VBM? Can I run those statistical using this asymmetric voxel data?</li>
<li>If it matters, how can I overcome the problem? I have FSL on my macbook and Matlab.</li>
<li>How about the effect of this asymmetric voxel for tractography?</li>
</ol>
</blockquote>
<br />
My response:<br />
<br />
<div>
<blockquote class="tr_bq">
That is a great question, I think it is indeed important to use isotropic voxels for DTI. You can run VBM and TBSS on non-isotropic voxels, interpolation will not likely alter your results, although isotropic acquisition might have. <br />
<br />
It is a different story for tractography, where it will be important to interpolate to isotropic voxels. Non-isotropic voxels can negatively influence fiber tracking results, eg. you are less likely to find specific smaller/bendier tracts. My personal experience when running different tests, is that higher (isotropic) resolution (even when through interpolation) will improve tractography results. Although you'll have to keep an eye on your computing power, it is easy to "break" your processor/RAM by going overboard with higher voxel resolution which will make visualizations tedious (although that doesn't become a problem till 0.5mm^3 and higher resolutions). <br />
<br />
I tried to find some references to support this, and although I know they must be out there, I couldn't quickly find any conclusive quotes. Somehow in most simulations that I found the authors assume isotropic voxels, while most clinical articles use non-isotropic voxels. Which is unfortunate.<br />
Some quotes:<br />
<blockquote class="tr_bq">
<span justify="" text-align:="">"FA values that are measured in regions containing crossing fibers are underestimated when using nonisotropic DTI."</span></blockquote>
<a href="http://www.ajnr.org/content/28/6/1102.short" style="color: #1155cc;" target="_blank">http://www.ajnr.org/content/<wbr></wbr>28/6/1102.short</a><br />
<blockquote class="tr_bq">
"<span justify="" text-align:="">The use of isotropic voxels is recommended to ensure that the accuracy of the tractography scheme is independent of fiber direction."</span></blockquote>
<a href="http://onlinelibrary.wiley.com/doi/10.1002/1522-2594(200010)44:4%3C625::AID-MRM17%3E3.0.CO;2-O/full" style="color: #1155cc;" target="_blank">http://onlinelibrary.wiley.<wbr></wbr>com/doi/10.1002/1522-2594(<wbr></wbr>200010)44:4%3C625::AID-MRM17%<wbr></wbr>3E3.0.CO;2-O/full</a><br />
<blockquote class="tr_bq">
"<span justify="" text-align:="">When selecting the acquisition parameters, one should strive for isotropic resolution since different in-plane and between-plane resolutions would lead to differential averaging of fiber orientations, leading to complicated modeling requirements if this is to be accounted for."</span></blockquote>
<a href="http://www.ncbi.nlm.nih.gov/pubmed/22846632" style="color: #1155cc;" target="_blank">http://www.ncbi.nlm.nih.gov/<wbr></wbr>pubmed/22846632</a></blockquote>
<div class="separator" style="clear: both; text-align: justified;">
In addition to the above references <a href="http://www.isi.uu.nl/People/?alexander" target="_blank">Alexander Leemans</a> directed my attention to this work:</div>
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<a href="http://cds.ismrm.org/protected/11MProceedings/files/1945.pdf" target="_blank"><img border="0" height="119" src="http://3.bp.blogspot.com/-B6m8LxofUok/VRQ7VxlhoSI/AAAAAAAAC1I/GLukGb8NQIc/s1600/Screen%2BShot%2B2015-03-26%2Bat%2B11.57.38%2BAM.png" width="320" /></a></div>
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<a href="http://cds.ismrm.org/protected/11MProceedings/files/1945.pdf">http://cds.ismrm.org/protected/11MProceedings/files/1945.pdf</a></div>
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<br /></div>
<h3>
What to do:</h3>
<div class="separator" style="clear: both; text-align: justified;">
These answers however do not address how you can alter you voxel size if you indeed would like to change your voxel dimensions to be more isotropic. <a href="http://www.paradime.net/" target="_blank">Sam Hurley</a> gave the great suggestions of using FSL's FLIRT to make this change. The nice thing about this tool is that you can stay within the FSL environment, but the down side it that it assumes you have a suitable reference image available with the correct dimensions. If you do not, you can instead decide to use other tools, like for example the ones developed by Gary Zhang for <a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Main.HomePage" target="_blank">DTI-TK</a>; SVResample and TVResample. The first will be able to resample scalar images (eg. the DWI images), while the latter is able to resample tensor images. Which tool you should use depends on what phase the data is in and when you want to alter the voxel dimensions.</div>
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<br /></div>
<div class="separator" style="clear: both; text-align: justified;">
The trick with either tool is to be considerate of the fact that resampling any dimension of your voxel space will inadvertently influence the size of the brain within the specified dimensions of the image. If you increase the voxel size, e.g. in the x direction from 1mm to 0.5mm, the space occupied by the brain will include twice as many slices. So where your image dimension used to be 248 slices it should now be 512. If you do not accommodate for this change the brain might get chopped off at the edges of the image.</div>
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Below is an worked out example of how to calculate the correct image dimension when altering specific image dimensions: </div>
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</div>
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<a href="http://4.bp.blogspot.com/-u59GKwnk0n0/VRLwwJ_sy1I/AAAAAAAAC0I/2kJePKWn4kg/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.29.19%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="165" src="http://4.bp.blogspot.com/-u59GKwnk0n0/VRLwwJ_sy1I/AAAAAAAAC0I/2kJePKWn4kg/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.29.19%2BPM.png" width="400" /></a></div>
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<a href="http://2.bp.blogspot.com/-SHf6KY9uUVo/VRLwwDV4dlI/AAAAAAAAC0E/LJu5rAKplo4/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.01%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="54" src="http://2.bp.blogspot.com/-SHf6KY9uUVo/VRLwwDV4dlI/AAAAAAAAC0E/LJu5rAKplo4/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.01%2BPM.png" width="320" /></a></div>
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<a href="http://3.bp.blogspot.com/-FHhrm1HKO1M/VRLwwDBpbjI/AAAAAAAAC0M/PU2JpPWVD4o/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.05%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="54" src="http://3.bp.blogspot.com/-FHhrm1HKO1M/VRLwwDBpbjI/AAAAAAAAC0M/PU2JpPWVD4o/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.05%2BPM.png" width="320" /></a></div>
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<a href="http://2.bp.blogspot.com/-FVmp1QadaiA/VRLwwnYAN8I/AAAAAAAAC0Q/6VmjnIh-lLM/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.11%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="54" src="http://2.bp.blogspot.com/-FVmp1QadaiA/VRLwwnYAN8I/AAAAAAAAC0Q/6VmjnIh-lLM/s1600/Screen%2BShot%2B2015-03-25%2Bat%2B12.30.11%2BPM.png" width="320" /></a></div>
<div style="text-align: center;">
<span style="color: #565656; font-family: inherit; font-size: 13px; font-style: inherit; line-height: 18px;">x = (256x1)/1 = 512</span></div>
<div style="text-align: center;">
<span style="color: #565656; font-family: inherit; font-size: 13px; font-style: inherit; line-height: 18px;">y = (256x1)/0.5 = 512</span></div>
<div style="text-align: center;">
<span style="color: #565656; font-family: inherit; font-size: 13px; font-style: inherit; line-height: 18px;">z = (64x4)/0.5 = 512</span></div>
</div>
<div>
<span style="color: #565656; font-size: x-small;"><span style="line-height: 18px;"><br />
</span></span></div>
<div>
The below code is just an example, there are more options available both within this tool and the TVResample tool that you can explore further:<br />
<br />
SVResample -in 019_bravo.nii.gz -out 019_final.nii.gz -xs 512 -ys 512 -zs 512 -xv 0.5 -yv 0.5 -zv 0.5<br />
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-49853315114647042082014-05-30T10:49:00.006-05:002015-09-01T14:58:12.862-05:00DTI processing with Brainvoyager<div class="separator" style="clear: both; text-align: center;"><br />
</div><div class="separator" style="clear: both; text-align: center;"><a href="http://support.brainvoyager.com/diffusion-weighted-imaging.html" target="_blank"><img border="0" src="http://4.bp.blogspot.com/-547V_4vRVQ4/VQSU3wcWhMI/AAAAAAAACnE/VETrEkMFYWM/s1600/Screen%2BShot%2B2015-03-14%2Bat%2B3.06.13%2BPM.png" height="60" width="640" /></a></div><div class="separator" style="clear: both; text-align: center;"><br />
</div><div class="separator" style="clear: both; text-align: justify;"><span style="line-height: 17px; text-align: start;"><a href="http://support.brainvoyager.com/diffusion-weighted-imaging.html" target="_blank">BrainVoyager QX</a> is a powerful neuroimaging software package. It started as a tool for the analysis of anatomical and functional MRI data sets but has evolved over the years into a multi-modal analysis tool for fMRI, DTI, TMS, EEG and MEG data. The software is highly optimized and user friendly running on all major computer platforms including Windows (XP/Vista/7), Linux (i.e. Ubuntu, SUSE, Fedora) and Mac OS X (10.6 or higher).</span></div><div><span style="color: #3366d1;"></span></div><br />
<a name='more'></a><div><br />
</div><h3><b>Overview of DWI Analysis Steps in BrainVoyager</b></h3><div><div style="line-height: 17px; margin-bottom: 0.5em; margin-top: 0.5em; padding: 0px;"><h4><b style="margin: 0px; padding: 0px;">Single-run data in measurement space:</b></h4></div></div><ul style="margin: 0.6em 1.5em 0em; padding: 0px 0px 0px 1em;"><li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/DMRProjectCreation.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Creating "DMR" projects using the Create Project Wizard</a></li>
<a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/DiffusionTensorEstimation.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Estimating diffusion tensors from DMR-DWI data</a></li>
<li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/ADCAndFAMapsOnMeasuredSlices.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Visualizing ADC and FA maps on measured slice planes</a></li>
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<div style="line-height: 17px; margin-bottom: 0.5em; margin-top: 0.5em; padding: 0px;"><br style="margin: 0px; padding: 0px;" /></div><div style="line-height: 17px; margin-bottom: 0.5em; margin-top: 0.5em; padding: 0px;"><h4><b style="margin: 0px; padding: 0px;">Single-run data in normalized 3D space:</b></h4></div><ul style="margin: 0.6em 1.5em 0em; padding: 0px 0px 0px 1em;"><li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/FromMeasuredSlicesTo3DSpace.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Alignment of DMR-DWI data with 3D VMR data set and transformation into ACPC or TAL space</a></li>
<li><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/DiffusionTensorEstimationIn3DSpace.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Estimating diffusion tensors from VMR-VDW data</a></li>
<li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/ADCFAAndColorDirectionMaps.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Visualizing ADC, FA and color direction maps</a></li>
<li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/DiffusionTensorVisualization.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Visualizing diffusion tensors</a></li>
<li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/InteractiveFiberTracking.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Interactive fiber tracking</a></li>
<li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/VOIBasedFiberTracking.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Fiber tracking from regions-of-interest (VOIs)</a></li>
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<div style="line-height: 17px; margin-bottom: 0.5em; margin-top: 0.5em; padding: 0px;"><br style="margin: 0px; padding: 0px;" /></div><div style="line-height: 17px; margin-bottom: 0.5em; margin-top: 0.5em; padding: 0px;"><h4><b style="margin: 0px; padding: 0px;">Group comparison:</b></h4></div><ul style="margin: 0.6em 1.5em 0em; padding: 0px 0px 0px 1em;"><li style="margin: 0.6em 0px; padding: 0px;"><a class="leaf" href="http://www.brainvoyager.com/bvqx/doc/UsersGuide/DWI/GroupAnalysisOfFAMaps.html" style="color: #000090; margin: 0px; padding: 0px; text-decoration: none;">Comparing FA maps across different groups using the ANCOVA module</a></li>
</ul><div><br />
</div><div><b>More information here:</b></div><a href="http://support.brainvoyager.com/diffusion-weighted-imaging.html">http://support.brainvoyager.com/diffusion-weighted-imaging.html</a><br />
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<a href="http://support.brainvoyager.com/diffusion-weighted-imaging.html" style="text-align: center;" target="_blank"><img border="0" src="http://1.bp.blogspot.com/-GL_a2ww4_p4/U4innG3OsdI/AAAAAAAAB4c/H2sdL9p5I9s/s1600/Screen+Shot+2014-05-30+at+10.45.05+AM.png" height="276" width="400" /></a><br />
<div style="font-size: 100%; text-align: center;"></div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-75259527428020081542014-04-15T12:25:00.002-05:002015-09-01T15:01:24.977-05:00Neuroscience Information Framework for DTI resources<div class="separator" style="clear: both; text-align: center;"><br />
</div><div class="separator" style="clear: both; text-align: center;"><a href="https://neuinfo.org/mynif/search.php?q=dti&t=registry" target="_blank"><img border="0" height="60" src="http://2.bp.blogspot.com/-fvqh8S-aCxk/VQSYcQUGnyI/AAAAAAAACnQ/dmgbTviLYXQ/s1600/Screen%2BShot%2B2015-03-14%2Bat%2B3.21.34%2BPM.png" width="640" /></a></div><div class="separator" style="clear: both; text-align: center;"><br />
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</div><div class="separator" style="clear: both; text-align: justified;">The Neuroscience Information Framework (NIF) is an initiative by NIH blueprint to bring together all knowledge on neuroscience. Of course DTI is part of this. Find everything NIF put together on DTI here:</div><div class="separator" style="clear: both; text-align: center;"><a href="https://neuinfo.org/mynif/search.php?q=dti&t=registry" target="_blank">https://neuinfo.org/mynif/search.php?q=dti&t=registry</a></div><div class="separator" style="clear: both; text-align: center;"></div><a name='more'></a><div style="text-align: left;"><br />
From their website (http://neuinfo.org/about/index.shtm): </div><br />
<h1 style="border: 0px; color: #013c41; font-size: 18px; line-height: 18px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">About NIF</h1><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">The Neuroscience Information Framework is a dynamic inventory of Web-based neuroscience resources: data, materials, and tools accessible via any computer connected to the Internet. An initiative of the NIH Blueprint for Neuroscience Research, NIF advances neuroscience research by enabling discovery and access to public research data and tools worldwide through an open source, networked environment.</div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;"><a href="http://youtu.be/DP8Zlbx8Las" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">View the Introductory Video about NIF. <img alt="NIF Video" class="textmiddle" src="http://neuinfo.org/images/video-icon50x50.png" height="35" style="border: 0px; margin: 0px; outline: 0px; padding: 0px; vertical-align: middle;" width="40" /></a></div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;"><br />
</div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;"><strong style="border: 0px; font-size: 13px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">NIF OFFERS:</strong></div><ol style="border: 0px; font-size: 12px; line-height: 18px; list-style-image: initial; list-style-position: initial; margin: 0px 0px 0px 25px; outline: 0px; padding: 0px; vertical-align: baseline;"><li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">A <a href="http://www.neuinfo.org/mynif/" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">search portal</a> for researchers, students, or anyone looking for neuroscience information, tools, data or materials.</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Access to content normally not indexed by search engines, i.e, the "hidden web"<http: nif="" nifgwt.html="" query="Stock%20Tg%28Zic4-EGFP%29" www.neuinfo.org=""></http:></li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Tools for resource providers to make resources more discoverable, e.g., ontologies, data federation tools vocabulary services</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;"><a href="http://neuinfo.org/nif_components/disco/interoperation.shtm" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">Tools</a> for promoting interoperability among databases</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;"><a href="https://confluence.crbs.ucsd.edu/display/NIF/NIF+Annotation+Standards" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">Standards</a> for data annotation</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">The <a href="http://neuinfo.org/vocabularies/index.shtm" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">NIFSTD ontology</a> covering the major domains of neuroscience, e.g., brain anatomy, cells, organisms, diseases, techniques</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;"><a href="http://neuinfo.org/developers/index.shtm" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">Services</a> for accessing the NIF vocabulary and NIF tools</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Best practices for creating discoverable and <a href="http://blog.neuinfo.org/index.php/category/interoperability" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">interoperable resources</a></li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Data annotation services: NIF experts can enhance your resource through semantic tagging (<a href="mailto:info@neuinfo.org" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">contact us</a>)</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">NIF cards: Easy links to neuroscience information from any web browser (See examples)</li>
<li style="border: 0px; margin: 5px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Ontology services: NIF knowledge engineers can help create or extend ontologies for neuroscience</li>
</ol><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">The NIF project is designed to serve the biomedical research community. The more participation from the community, the better the NIF. We welcome all <a href="mailto:support@neuinfo.org" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">feedback and suggestions</a> and are actively looking for resource providers to make their resources accessible through the NIF. We offer a variety of methods for making resources searchable through the NIF. For more information or to recommend a resource, follow the link to <a href="http://neuinfo.org/cindylite/nominate.html" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;" target="_blank">Register Your Resource</a>, or send us an e-mail at<a href="mailto:curation@neuinfo.org." style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">curation@neuinfo.org</a>.</div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">Collaborating institutions working on the current version of NIF include University of California, San Diego, California Institute of Technology, George Mason University, Yale University Medical College, and Washington University.</div><h2 style="border: 0px; color: #013c41; font-size: 15px; line-height: 18px; margin: 10px 0px; outline: 0px; padding: 0px; vertical-align: baseline;">The History of NIF</h2><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">Established in 2004, the NIH Blueprint for Neuroscience Research brings the 16 NIH Institutes, Centers and Offices that support neuroscience research into a collaborative framework to coordinate their ongoing efforts and to plan new cross-cutting initiatives. Working together, representatives from the partner Institutes, Centers, and Offices identify pervasive challenges in neuroscience and any technological barriers to solving them.</div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">Early in their deliberations Blueprint representatives recognized that a framework for identifying, locating, relating, accessing, integrating, and analyzing information from the neuroscience research enterprise is critical to enhancing cooperative activities in the neurosciences. A Broad Agency Announcement was issued, and in 2005, the Blueprint began support for a new initiative known as the "Neuroscience Information Framework" (NIF).</div><div style="border: 0px; font-size: 12px; line-height: 18px; margin-bottom: 10px; margin-top: 10px; outline: 0px; padding: 0px; vertical-align: baseline;">The first phase of NIF was led by Dr. Dan Gardner at Weill Cornell Medical College. During this phase, the NIF produced a<a href="http://neuinfo.org/about/publications/nif_knowledge_environment.pdf" style="border: 0px; color: #4b8d8d; margin: 0px; outline: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">white paper</a> outlining the current state of neuroscience resources. The project also produced a registry of neuroscience-relevant resources, an extensive vocabulary for describing resources and their content, and an initial version of a concept-based query system that could be used to query across diverse types of resources. The current phase of NIF is led by Dr. Maryann Martone at UCSD. This phase is devoted to hardening and maintaining the NIF while continuing to expand its resources and increase its usability and functionality.<br />
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<a href="https://neuinfo.org/mynif/search.php?q=dti&t=registry" style="font-family: Times; line-height: normal; text-align: center;" target="_blank"><img border="0" height="190" src="http://3.bp.blogspot.com/-1lYjvP4MzfM/Uq9nVp5ASYI/AAAAAAAABmI/8hYx2qMg6vU/s400/Screen+Shot+2013-12-16+at+2.48.47+PM.png" width="400" /></a></div><div style="font-size: 100%; text-align: justify;"></div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-46102473995440100492014-04-04T11:19:00.000-05:002015-10-13T12:33:34.635-05:00Calculate the diffusion tensor from diffusion weighted images: A mathematical example<div style="margin-bottom: 24px;">
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<a href="http://3.bp.blogspot.com/-tp85GeS8wWI/VQSaTXeEQaI/AAAAAAAACnc/yWPGXoaqfrU/s1600/Screen%2BShot%2B2015-03-14%2Bat%2B3.29.40%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="265" src="http://3.bp.blogspot.com/-tp85GeS8wWI/VQSaTXeEQaI/AAAAAAAACnc/yWPGXoaqfrU/s1600/Screen%2BShot%2B2015-03-14%2Bat%2B3.29.40%2BPM.png" width="640" /></a></div>
<span style="color: #2b2b2b;"><span style="line-height: 24px;"><br />
</span></span> <span style="color: #2b2b2b;"><span style="line-height: 24px;">A guest post by </span></span><a href="http://www.linkedin.com/pub/rodrigo-perea/12/583/238" style="color: #2b2b2b;" target="_blank">Rodrigo Dennis Perea</a><span style="color: #2b2b2b;"><span style="line-height: 24px;"> on</span></span><span style="color: #2b2b2b;"><span style="line-height: 24px;"> the computational models for estimating the diffusion tensor from diffusion weighted images:</span></span><span style="color: #2b2b2b;"><span style="line-height: 24px;"> </span></span></div>
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<span style="color: #2b2b2b; line-height: 24px;">"When I first started working with DTI, I had a hard time understanding the derivation of the diffusion tensor from what was acquired on the scanner, the diffusion weighted image (DWI). Once I understood this process a little better I decided to create a brief review. I hope this will help you! It might be lacking information or it might be too implicit so please feel free to ask me anything if you need more detailed information."</span></div>
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<span style="color: #2b2b2b; line-height: 24px; text-align: left;">I have to thank Peter B. Kingsley for his very helpful publication on this topic: </span><a href="http://onlinelibrary.wiley.com/doi/10.1002/cmr.a.20048/abstract%C2%A0" style="line-height: 24px; text-align: left;" target="_blank">http://onlinelibrary.wiley.com/doi/10.1002/cmr.a.20048/abstract </a></div>
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<span style="color: #2b2b2b; line-height: 24px;">First, it is worth noting that this example will highlight the computations in a single voxel (e.g. </span>x=76, y=64, z=21)<span style="color: #2b2b2b; line-height: 24px;">. Essentially, a similar approach will be applied to every voxel in the entire MRI scan. </span><span style="color: #2b2b2b; line-height: 24px;">The figure below illustrates our example where we have six DWI's, and the sample voxel is denoted by a small color coded square: </span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://2.bp.blogspot.com/-sMOP9b7ZAaY/Uzt0x04SdCI/AAAAAAAABuE/bY9IbuftkkE/s3200/six_brains.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="400" src="http://2.bp.blogspot.com/-sMOP9b7ZAaY/Uzt0x04SdCI/AAAAAAAABuE/bY9IbuftkkE/s400/six_brains.png" width="310" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Diffusion weighted images</td></tr>
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The simplified diffusion matrix will look like this:</div>
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<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-Jh4_fCPMFqg/UzuA-_v84uI/AAAAAAAABuU/sG2kIUpQ0JI/s3200/dees.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="150" src="http://1.bp.blogspot.com/-Jh4_fCPMFqg/UzuA-_v84uI/AAAAAAAABuU/sG2kIUpQ0JI/s320/dees.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Diffusion matrix</td></tr>
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Which will translate into this tensor:</div>
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<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-fR6uK-iMwTU/UzuBCM_OPRI/AAAAAAAABuc/cNywjgd2bJw/s3200/tensor.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="http://4.bp.blogspot.com/-fR6uK-iMwTU/UzuBCM_OPRI/AAAAAAAABuc/cNywjgd2bJw/s320/tensor.png" width="244" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Tensor model</td></tr>
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<span style="color: #2b2b2b; line-height: 24px;">Next the Stejskal-Tanner equation is of importance, this equation shows how there will be a reduction in the signal due to the application of a pulse gradient, this change will be proportional to the amount of diffusion: </span></div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png" href="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png" style="color: #24890d; line-height: 24px; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png?w=118" src="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png?w=118" height="55" id="i-17" style="max-width: 474px; vertical-align: middle;" width="200" /></a><span style="color: #2b2b2b; line-height: 24px;">[1] </span></div>
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<span style="color: #2b2b2b; line-height: 24px;">It is important to notice that we will solve for tensor D given the other parameters from the MRI acquisition. Sk is the intensity at the "single voxel" when a specific gk gradient direction is applied (e.g. a 1x3 vector for the x,y,z direction). b0 is the no diffusion signal and S0 is the intensity value you get from the no-diffusion signal. So lets begin...</span></div>
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A random MRI-DWI sequence (lets say sample_001) is used as an example. Given the following gradient vectors with their specific intensity values at an <i>specific voxel location</i> in the brain, we will derive equation [1] to solve for the simplest case with only 7 volumes (1 3D volume with no diffusion and 6 3D volumes with pulse gradient directions).</div>
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<span data-mce-style="text-decoration: underline;" style="font-family: Georgia, 'Times New Roman', serif; text-decoration: underline;">A simple approach: The “H” approach</span><span style="font-family: Georgia, 'Times New Roman', serif;">: Six gradient directions (S</span><span style="font-family: Georgia, 'Times New Roman', serif; font-size: 12px; height: 0px; line-height: 0; position: relative; top: 0.5ex; vertical-align: baseline;">k</span><span style="font-family: Georgia, 'Times New Roman', serif;">) images “plus” a no-diffusion image (S</span><span style="font-family: Georgia, 'Times New Roman', serif; font-size: 12px; height: 0px; line-height: 0; position: relative; top: 0.5ex; vertical-align: baseline;">0</span><span style="font-family: Georgia, 'Times New Roman', serif;">)</span></div>
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Given the following gradient direction pulses and intensities with a b-value of 800 s/mm<span style="bottom: 1ex; font-size: 12px; height: 0px; line-height: 0; position: relative; vertical-align: baseline;">2</span>:</div>
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Given equation Stejskal-Tanner equation:</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png" href="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png?w=118" src="http://dtibyexample.files.wordpress.com/2014/03/stejkalequation1.png?w=118" height="55" id="i-17" style="max-width: 474px; vertical-align: middle;" width="200" /></a>[1] </div>
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with <span style="font-weight: 700;">g<span style="font-size: 12px; height: 0px; line-height: 0; position: relative; top: 0.5ex; vertical-align: baseline;">k</span></span><span style="font-size: 12px; height: 0px; line-height: 0; position: relative; top: 0.5ex; vertical-align: baseline;"></span>:</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/gks.png" href="http://dtibyexample.files.wordpress.com/2014/03/gks.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/gks.png?w=128" src="http://dtibyexample.files.wordpress.com/2014/03/gks.png?w=128" height="62" id="i-18" style="height: auto; max-width: 474px; vertical-align: middle;" width="200" /></a> [2] </div>
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And <span style="font-weight: 700;">D</span>:<span style="font-weight: 700;"></span></div>
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<span style="font-weight: 700;"><a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/dtensor.png" href="http://dtibyexample.files.wordpress.com/2014/03/dtensor.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/dtensor.png?w=159" src="http://dtibyexample.files.wordpress.com/2014/03/dtensor.png?w=159" height="88" id="i-19" style="height: auto; max-width: 474px; vertical-align: middle;" width="200" /></a> </span>[3]<span style="font-weight: 700;"></span></div>
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We can solve expand equation [1] in terms of <span style="font-weight: 700;">D</span>:</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/solvingstejkal.png" href="http://dtibyexample.files.wordpress.com/2014/03/solvingstejkal.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/solvingstejkal.png?w=487" src="http://dtibyexample.files.wordpress.com/2014/03/solvingstejkal.png?w=487" id="i-20" style="height: auto; max-width: 474px; vertical-align: middle;" /></a> [4]</div>
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Where the subscript “i” denotes each gradient direction pulse ("i" in this case will go from 1 to 6). One approach to solve this system of equation is to use matrix algebra. Thus let express <span style="font-weight: 700;">D</span> as a six-element column vector, <span style="font-weight: 700;">d</span>:</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/ds.png" href="http://dtibyexample.files.wordpress.com/2014/03/ds.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/ds.png?w=227" src="http://dtibyexample.files.wordpress.com/2014/03/ds.png?w=227" id="i-21" style="height: auto; max-width: 474px; vertical-align: middle;" /></a> [5]</div>
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With a six-element row matrix <span style="font-weight: 700;">H </span>containing a large M x 6 matrix, where M is the number of gradient directions (in this case M=6):</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/h_6.png" href="http://dtibyexample.files.wordpress.com/2014/03/h_6.png" style="color: #24890d; text-decoration: none;"><img alt="H_6" class="alignnone size-medium wp-image-31" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/h_6.png?w=300" src="http://dtibyexample.files.wordpress.com/2014/03/h_6.png?w=300" height="78" style="height: auto; max-width: 474px; vertical-align: middle;" width="300" /></a>[6]</div>
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Lastly, let’s define a Y matrix for the left side of equation [4]:</div>
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<a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/y_7.png" href="http://dtibyexample.files.wordpress.com/2014/03/y_7.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/y_7.png?w=168" src="http://dtibyexample.files.wordpress.com/2014/03/y_7.png?w=168" id="i-22" style="height: auto; max-width: 474px; vertical-align: middle;" /></a>[7]</div>
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Thus we can express equation [4] with the following 6 directions as:</div>
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<span style="font-weight: 700;"><a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/y_8.png" href="http://dtibyexample.files.wordpress.com/2014/03/y_8.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/y_8.png?w=61" src="http://dtibyexample.files.wordpress.com/2014/03/y_8.png?w=61" id="i-23" style="height: auto; max-width: 474px; vertical-align: middle;" /></a> </span>[8]</div>
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<span style="color: #2b2b2b; line-height: 24px;">With exactly six directions, there is an exact analytic solution and can be solved using standard methods such as the Cramer’s rule</span><span style="color: #2b2b2b; line-height: 24px;">:</span></div>
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<span style="font-weight: 700;"><a data-mce-href="http://dtibyexample.files.wordpress.com/2014/03/h_invh_9.png" href="http://dtibyexample.files.wordpress.com/2014/03/h_invh_9.png" style="color: #24890d; text-decoration: none;"><img alt="Image" class="size-full wp-image" data-mce-src="http://dtibyexample.files.wordpress.com/2014/03/h_invh_9.png?w=165" src="http://dtibyexample.files.wordpress.com/2014/03/h_invh_9.png?w=165" id="i-24" style="height: auto; max-width: 474px; vertical-align: middle;" /></a> </span>[9]</div>
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Using these equation, I was able to create functions in matlab. I also compared my accurate results with FSL dtifit, which gave me similar results. </div>
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I'll be more than happy to share my matlab functions and explain these in more detailed if needed. </div>
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Anonymoushttp://www.blogger.com/profile/16748644907007547701noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-9107450496672682082014-04-02T00:17:00.000-05:002015-10-09T00:09:27.935-05:00DTI Quality Control - Part 4: Further reading<div style="border: 0px; margin: 0px; padding: 0px; vertical-align: baseline;">
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<img border="0" height="211" src="http://2.bp.blogspot.com/-ZfnbyUAPHJE/VQWeZpmMp8I/AAAAAAAACn4/DwF1L9txdeA/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B9.59.26%2BAM.png" width="640" /></div>
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</span> <span style="font-family: Georgia, Times New Roman, serif;">Analysis of diffusion images are complicated by the fact that errors </span><span style="font-family: Georgia, 'Times New Roman', serif;">can be easily hidden inside the calculated tensor data and not clearly show up in your final processed FA image. Quality control is thus an essential step to ensure data quality. Three previous blog post were dedicated to <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-1-acquisition.html" target="_blank">quality control after acquisition</a>, <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-2-tensor.html" target="_blank">during tensor calculation</a> and <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-3-tools.html" target="_blank">suggested tools to help with QC</a>. </span><span style="font-family: Georgia, 'Times New Roman', serif;">This post will suggest some articles for further reading, mainly focussed on best practice during quality control. </span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">To start off I would like to </span><span style="background-color: transparent; font-family: Georgia, 'Times New Roman', serif;">suggest <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3366862/" target="_blank">this excellent article </a>by </span><span style="font-family: Georgia, Times New Roman, serif;"><a href="http://www.findanexpert.unimelb.edu.au/display/person146046" target="_blank">Tournier</a>, <a href="http://www.cis.jhu.edu/faculty/smori.php" target="_blank">Mori</a> and <a href="http://www.isi.uu.nl/People/?alexander" target="_blank">Leemans</a>.</span><span style="font-family: Georgia, Times New Roman, serif;"> </span><span style="background-color: transparent; font-family: Georgia, 'Times New Roman', serif;">In this publication the authors discuss a number of issues that you can pay attention to. Like the examples from figure 1 in their article. </span><span style="color: #222222; font-family: Georgia, 'Times New Roman', serif;">There are more examples in their </span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3366862/" style="font-family: Georgia, 'Times New Roman', serif;" target="_blank">full article </a><span style="color: #222222; font-family: Georgia, 'Times New Roman', serif;">that you can check out. </span></div>
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<span style="font-family: Georgia, Times New Roman, serif; margin-left: 1em; margin-right: 1em;"><a href="http://4.bp.blogspot.com/-rT6RxPU6wco/UVHGTwwmy0I/AAAAAAAABZI/3lKGG14pWXM/s1600/Screen+Shot+2013-03-26+at+9.50.45+AM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="296" src="http://4.bp.blogspot.com/-rT6RxPU6wco/UVHGTwwmy0I/AAAAAAAABZI/3lKGG14pWXM/s640/Screen+Shot+2013-03-26+at+9.50.45+AM.png" width="400" /></a></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">You might also like the aptly named <a href="http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00031/full" target="_blank">hitchhiker's guide to diffusion tensor imaging</a> - previously discussed in <a href="http://www.diffusion-imaging.com/2013/03/list-of-software-tools-for-dti.html" target="_blank">this post</a>. They give a clear overview of steps important for effective diffusion imaging processing and quality control:</span></div>
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<a href="http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00031/full" target="_blank"><img border="0" height="320" src="http://1.bp.blogspot.com/-W0mcDxdroEA/UzuQZGaicoI/AAAAAAAABvE/UrLGEvpuqmY/s3200/fnins-07-00031-g001.jpg" width="278" /></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><span style="color: #222222;">An other previously discussed (on the <a href="https://www.facebook.com/DiffusionImaging/posts/754554684570699" target="_blank">diffusion imaging Facebook page</a>, check it out!) article is written by <a href="http://psych.cf.ac.uk/contactsandpeople/academics/jonesderek.php" target="_blank">Derek Jones</a> <i>et al.</i> and discusses the <a href="http://www.sciencedirect.com/science/article/pii/S1053811912007306" target="_blank">do's and don'ts of diffusion MRI</a>. Less involved with practical quality control tips, and thus more involved with global expectation management and best use practices:</span></span></div>
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<span style="color: #2e2e2e; line-height: 20px; word-spacing: -1px;"><span style="font-family: Georgia, Times New Roman, serif;">" In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of ‘Do's and Don'ts’ which define good practice in this expanding area of imaging neuroscience."</span></span></blockquote>
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A recent <a href="http://www.worldneurosurgery.org/article/S1878-8750(13)00011-9/abstract" target="_blank">paper</a> by a german neurosurgery group gives a comprehensive overview of software packages that can be used for fiber tracking and compares their efficacy running different fiber tracking algorithms. Results are reviewed by a physicist, neuroradiologist and neurosurgeon (lower scores are better):</div>
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<a href="http://www.worldneurosurgery.org/article/S1878-8750(13)00011-9/abstract" target="_blank"><img border="0" height="243" src="http://2.bp.blogspot.com/-PaBb4dOMIWU/UzuWfK8FDTI/AAAAAAAABvU/4PNH53zTbcc/s3200/Screen+Shot+2014-04-01+at+11.45.47+PM.png" width="400" /></a></div>
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It is clearly not easy to acquire diffusion images without error, the successful acquisition of diffusion imaging data in neonates is a factor more complicated. <a href="http://www.ajnr.org/content/34/8/1496.short" target="_blank">This article</a> give some important tips for acquisition guidelines - for example note that gray/white matter contrast is not nearly as pronounced yet, and sometimes looks opposite in brightness as compared to older populations. This is due to reduced myelination of the white matter at this stage, and can be countered by changing the relaxation time of the T1 or T2 scan. They also go into details of effective quality assessment, to catch issues arising due to movement, pulsation or reduced head size.</div>
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<a href="http://www.ajnr.org/content/34/8/1496.short" target="_blank"><img border="0" height="240" src="http://4.bp.blogspot.com/-YPGROrA9NGY/Uzua_JXo-sI/AAAAAAAABvg/2JXM5-lFy5I/s3200/Screen+Shot+2014-04-01+at+11.51.30+PM.png" width="320" /></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Finally, a group at Vanderbilt suggest a number of complementary metrics that can be used for quality assurance and compare their unified pipeline with visual inspection and show that their method can produce a 70% time saving. </span><span style="font-family: Georgia, 'Times New Roman', serif;">More information on their methods in </span><a href="http://www.ncbi.nlm.nih.gov/pubmed/23637895" style="font-family: Georgia, 'Times New Roman', serif;" target="_blank">their article</a><span style="font-family: Georgia, 'Times New Roman', serif;">.</span></div>
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<a href="http://www.ncbi.nlm.nih.gov/pubmed/23637895" target="_blank"><img border="0" height="320" src="http://4.bp.blogspot.com/-ukB76Z23Pyg/UzuMoBPXynI/AAAAAAAABu4/mxpd4igwjx8/s3200/Screen+Shot+2014-04-01+at+11.04.58+PM.png" width="270" /></a></div>
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Thanks to <a href="http://www.waisman.wisc.edu/pi-alexander.htm" target="_blank">Andy Alexander</a> at the <a href="http://brainimaging.waisman.wisc.edu/" target="_blank">Waisman Center for brain imaging and behavior</a> for his database on QC articles.</div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-30605604807409997312014-03-06T22:39:00.000-06:002015-10-09T00:10:00.299-05:00DTI Quality Control - Part 3: Tools<div class="separator" style="clear: both; text-align: center;">
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<img border="0" height="186" src="http://1.bp.blogspot.com/-zmg3qKvz6Kw/VQWiL2ZA5HI/AAAAAAAACoI/Kwz_3hWE1AM/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B10.15.38%2BAM.png" width="640" /></div>
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</span> <span style="background-color: white; color: #222222; font-family: Georgia, Times New Roman, serif;">Question:</span><br />
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<span style="font-family: Georgia, Times New Roman, serif;">"We wish to know if there is a quality control program we could run the initial DTI data for each subject through to give us some sort of objective metric output about its quality." </span> Deborah L. Kerr, Ph.D.</blockquote>
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<span style="font-family: Georgia, Times New Roman, serif;">Diffusion imaging quality insurance is very important (as discussed in <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-1-acquisition.html" target="_blank">this</a> and <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-2-tensor.html" target="_blank">this</a> post) and there are a few - but not nearly enough - tools that can help with that. </span><span style="font-family: Georgia, 'Times New Roman', serif;">A fairly new tool is DTIPrep;</span></div>
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<span style="line-height: 21.599998474121094px;"><span style="font-family: Georgia, Times New Roman, serif;">DTIPrep is the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies.</span></span></blockquote>
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<a href="http://www.na-mic.org/Wiki/index.php/Projects:DTI_DWI_QualityControl" target="_blank"><img border="0" height="302" src="http://3.bp.blogspot.com/-K6zKd1CQTmw/UxkHVn81BQI/AAAAAAAABrI/7zGQoGk4Mlk/s1600/fninf-08-00004-g0004.jpg" width="400" /></a><span id="goog_921383815"></span><span id="goog_921383816"></span><a href="https://www.blogger.com/"></a></div>
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<a name='more'></a>It is able to do:<br />
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<li><span style="font-family: Georgia, 'Times New Roman', serif;">Dicom to NRRD converting</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Image info checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Diffusion information checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Rician LMMSE noise filter</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Slice-wise intensity checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Interlace-wise intensity checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Averaging baseline images</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Eddy current and motion correction</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Gradient-wise checking of residual motion/deformations</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Joint rician LMMSE noise filter</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Brain masking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">DTI computing</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Dominant direction artifact (vibration artifact) checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Optional visual checking</span></li>
<li><span style="font-family: Georgia, 'Times New Roman', serif;">Simulation-based bias analysis</span></li>
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<span style="font-family: Georgia, Times New Roman, serif;"><span style="color: #222222;">It is unfortunately at this time not yet able to implement a fieldmap correction. Hopefully this will be added soon.</span></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><span style="color: #222222;">For more information check out their website: </span></span><a href="http://www.na-mic.org/Wiki/index.php/Projects:DTI_DWI_QualityControl" style="color: #1155cc;" target="_blank"><span style="font-family: Georgia, Times New Roman, serif;">http://www.na-mic.org/Wiki/<wbr></wbr>index.php/Projects:DTI_DWI_<wbr></wbr>QualityControl</span></a></div>
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<span style="color: #222222;"><span style="font-family: Georgia, Times New Roman, serif;">Or their publication: <a href="http://www.ncbi.nlm.nih.gov/pubmed/24523693">http://www.ncbi.nlm.nih.gov/pubmed/24523693</a></span></span></div>
<span style="font-family: Georgia, Times New Roman, serif;">Download it here: </span><a href="http://www.nitrc.org/projects/dtiprep/" style="color: #1155cc; font-family: Georgia, 'Times New Roman', serif;" target="_blank">http://www.nitrc.org/projects/<wbr></wbr>dtiprep/</a><br />
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<a href="http://cmic.cs.ucl.ac.uk/camino/" target="_blank"><img border="0" height="200" src="http://2.bp.blogspot.com/-VgFKv3KwF9w/Uxk-F1DafmI/AAAAAAAABrY/dT_my79GWVA/s1600/caminologo.png" width="197" /></a></div>
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</span> <span style="font-family: Georgia, 'Times New Roman', serif;">A different toolbox, called <a href="http://cmic.cs.ucl.ac.uk/camino/" target="_blank">Camino</a>, helps you estimate the signal to noise ratio (SNR) and noise variance of your diffusion image. The tool is called <a href="http://cmic.cs.ucl.ac.uk/camino//index.php?n=Man.Estimatesnr" target="_blank">estimatesnr</a>. </span><span style="background-color: white; color: #222222; font-family: Georgia, 'Times New Roman', serif;">Their explanation is somewhat complicated but what it comes down to is this -</span><br />
<span style="background-color: white; color: #222222;"><span style="font-family: Georgia, Times New Roman, serif;">If you have 2 B0 images: </span></span><br />
<blockquote class="tr_bq" style="color: #505050; padding-left: 10px;">
<span style="font-family: Georgia, Times New Roman, serif;">The traditional method for estimating the noise is to sample two ROIs, one in brain white matter, and one in the background. Assuming that the background signal contained only noise, we can estimate the noise standard deviation as</span><br />
<blockquote class="tr_bq">
<span style="font-family: Georgia, Times New Roman, serif;"> sigma = sqrt(2.0 / (4.0 - PI)) * stddev(signal in background region)</span></blockquote>
<span style="font-family: Georgia, Times New Roman, serif;">where the constant scaling corrects for the Rician distribution of the noise, giving us the standard deviation sigma of the original signal. To synthesize data with the same noise conditions, we would take the true signal S_0 and calculate</span><br />
<blockquote class="tr_bq">
<span style="font-family: Georgia, Times New Roman, serif;"> S = |[S_0 + N(0, sigma), N(0, sigma)]|</span></blockquote>
<span style="font-family: Georgia, Times New Roman, serif;">where N(0, sigma) is a random sample drawn from normal distribution with mean 0 and standard deviation sigma.</span></blockquote>
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If you have more than 2 B0 images:<br />
<blockquote class="tr_bq" style="color: #505050; padding-left: 10px;">
<span style="font-family: Georgia, Times New Roman, serif;">The second method requires multiple b=0 images, and defines sigma_mult as the standard deviation of the signal over the ROI, across all K b=0 images. Again, let i be a voxel index, then</span><br />
<blockquote class="tr_bq">
<span style="font-family: Georgia, Times New Roman, serif;"> sigma_i = stddev(S_{i1},...,S{iK}))</span><span style="font-family: Georgia, Times New Roman, serif;"> sigma_mult = mean(sigma_1,...,sigma_N)</span></blockquote>
<span style="font-family: Georgia, Times New Roman, serif;">And finally SNR is</span><br />
<blockquote class="tr_bq">
<span style="font-family: Georgia, Times New Roman, serif;"> mean(S_{11}, S_{12},...,S_{1K}, S_{21},...,S_{NK}) / sigma_mult</span></blockquote>
<span style="font-family: Georgia, Times New Roman, serif;">If there are two or more b=0 images, both snr_diff and snr_mult will both be computed. The more b=0 images there are, the better the estimate via sigma_mult, but sigma_diff only ever uses the first two b=0 images.</span></blockquote>
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<span style="font-family: Georgia, Times New Roman, serif;">You can use a combination of SNR and maximum intensity of the DWI image - as extracted with <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils" target="_blank">fslstats</a> option -r - to get insight into the quality of the data:</span><br />
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-89414347912277938532014-03-06T10:31:00.000-06:002015-10-09T00:10:16.818-05:00DTI Quality Control - Part 2: Tensor fitting<div>
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<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">After making sure your data was acquired correctly (see also <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-1-acquisition.html" target="_blank">this post</a></span><span style="color: #494949;"><span style="line-height: 20.24639892578125px;">) and all looked good - you can go ahead and start tensor fitting. At the core of tensor fitting is using the right gradient directions and sometimes (for programs like Camino) creating a so called "scheme file". A scheme file consists of both the b-values and b-vectors. The b-values are the amount of diffusion weighting used for each volume. Depending on how many non-diffusion weighted or B0 scans you collect (read more on its use <a href="http://www.diffusion-imaging.com/2013/02/why-do-we-acquire-b0-images-in-dti-exams.html" target="_blank">here</a></span><span style="line-height: 20.24639892578125px;">) and what diffusion weighting max you use your b-value file is going to look something like this (shortened version):</span></span> <br />
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<span style="background-color: black; color: white; font-family: Courier New, Courier, monospace;">0,0,0,0,0,0,1000,1000,1000,1000,1000,1000,1000,1000,1000,1000,1000</span></div>
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<span style="color: #494949;"><span 20.24639892578125px="" line-height:="">Your b-vector are the gradient directions that you collect, often predetermined by the scanner depending on how many total directions you choose to collect. A b-vector file will look something like this (shortened version):</span></span></div>
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<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-MGAxpSHgzuI/UxajYlKfSDI/AAAAAAAABps/qgdh5OoCyJA/s1600/Screen+Shot+2014-03-04+at+10.08.50+PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="400" src="http://3.bp.blogspot.com/-MGAxpSHgzuI/UxajYlKfSDI/AAAAAAAABps/qgdh5OoCyJA/s400/Screen+Shot+2014-03-04+at+10.08.50+PM.png" width="317" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">b-vectors</td></tr>
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</span></span></span> <span style="color: #494949;"><span style="line-height: 20.24639892578125px;"><span style="color: #494949;">The b-vectors consist out of 3 separate vectors (x, y, z) for each direction acquired. A program like fsl2scheme (more info <a href="http://web4.cs.ucl.ac.uk/research/medic/camino/pmwiki/pmwiki.php?n=Man.Fsl2scheme" target="_blank">here</a>) </span><span style="color: #494949;">will be able to combine the b-values and b-vectors into a scheme file. Together looking something like this (note that there are 4 b0 slices visible, while the scheme file only shows 1 b0):</span></span></span></div>
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<tr><td class="tr-caption" style="text-align: center;">Scheme file, next to diffusion volumes</td></tr>
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</span></span> <span style="color: #494949;"><span style="line-height: 20.24639892578125px;">If you run a eddy correction it is advisable to correct your scheme file for the induced movement of each volume after registration. Here are a couple of resources to help you with this: </span></span></div>
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<span style="color: #494949; line-height: 20.24639892578125px;"><a href="http://blog.cogneurostats.com/?p=302">http://blog.cogneurostats.com/?p=302</a></span><br />
<span style="color: #494949; line-height: 20.24639892578125px;"><a href="http://onlinelibrary.wiley.com/doi/10.1002/mrm.21890/abstract">http://onlinelibrary.wiley.com/doi/10.1002/mrm.21890/abstract</a></span><br />
<span style="color: #494949;"><span 20.24639892578125px="" line-height:=""><a href="https://github.com/bernardng/codeSync/blob/master/dMRIanalysis/rotatebvecs">https://github.com/bernardng/codeSync/blob/master/dMRIanalysis/rotatebvecs</a></span></span><br />
<span style="color: #494949;"><span 20.24639892578125px="" line-height:=""><a href="https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3c23f260.0903">https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3c23f260.0903</a></span></span><br />
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<span style="color: #494949;"><span style="line-height: 20.24639892578125px;"><span style="background-color: white;">After running the tensor fitting command you will have to check if the vectors were applied correctly. </span></span></span><span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">In the below examples we are using </span><a href="http://web4.cs.ucl.ac.uk/research/medic/camino/pmwiki/pmwiki.php?n=Man.Pdview" style="background-color: white; line-height: 20.24639892578125px;" target="_blank">Camino's pdview</a><span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">, but you can use any program that allows you to view the tensors. You should </span><span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">check the axial, coronal and sagittal views. As a reference the corpus callosum can be used, the tensors should follow the shape of the corpus callosum (in red) in a fluid motion, as in the examples below:</span></div>
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<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-9OrnC2y2A4M/UxjNw53BQTI/AAAAAAAABq0/y5RCqnm9-Yc/s1600/86e1b.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="400" src="http://3.bp.blogspot.com/-9OrnC2y2A4M/UxjNw53BQTI/AAAAAAAABq0/y5RCqnm9-Yc/s400/86e1b.png" width="363" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Axial view</td></tr>
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<tr><td style="text-align: center;"><a href="http://2.bp.blogspot.com/-0nOZ0Vb01wE/UxjNquwWj2I/AAAAAAAABqs/9JINBlKJ0Ko/s1600/b05be.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="400" src="http://2.bp.blogspot.com/-0nOZ0Vb01wE/UxjNquwWj2I/AAAAAAAABqs/9JINBlKJ0Ko/s400/b05be.png" width="363" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Coronal view</td></tr>
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<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-5-26aw9cOtU/UxjNlpJyZoI/AAAAAAAABqk/yLVbaPtksIY/s1600/89bba.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="400" src="http://3.bp.blogspot.com/-5-26aw9cOtU/UxjNlpJyZoI/AAAAAAAABqk/yLVbaPtksIY/s400/89bba.png" width="362" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Sagittal view</td></tr>
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<span font-size:="" small=""><span style="line-height: 1.3;"> EXAMPLES OF WRONG ORIENTATION:</span></span></h3>
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<span style="font-size: small;">Notice the tensor orientations in corpus callosum (red area)</span></div>
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<span font-size:="" small=""> Fix it by adjusting the scheme file:</span></h3>
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There are multiple options to correct the scheme file in the fsl2scheme command:</div>
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- add <b>-usegradmod</b>(usually when error pops up)</div>
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<span style="background-color: transparent;">- add </span><b style="background-color: transparent;">-flipx</b></div>
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<span style="background-color: transparent;"><b>- </b>add </span><b style="background-color: transparent;">-flipy</b></div>
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<span style="background-color: transparent;"><b>- </b>add </span><b style="background-color: transparent;">-flipz</b></div>
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<span style="background-color: transparent;"><b>- </b>or any combination of the above, eg. as used in the example above: </span><b style="background-color: transparent;">fsl2scheme -bvecfile bvecs.txt -bvalfile bvals.txt -flipx -flipy -usegradmod prefix.scheme</b></div>
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<span font-size:="" small=""> EXAMPLE OF WRONG COLOR:</span></h3>
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Notice the blue corpus callosum</div>
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<span font-size:="" small="">Fix it by adjusting the b-vector table:</span></h3>
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- In the bvecs text file, swap the x and z row with the directions.</div>
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<span style="background-color: transparent;">- And in this specific case: </span><b style="background-color: transparent;">fsl2scheme -bvecfile bvecs_zx.txt -bvalfile bvals.txt -flipx -flipz -usegradmod prefix.scheme</b></div>
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<span font-size:="" small="">ANOTHER EXAMPLE:</span></h4>
<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">Notice the green corpus callosum</span><br />
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<tr><td class="tr-caption" style="text-align: center;">Wrong orientation</td></tr>
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<h3 style="font-weight: 500; line-height: 1.4; margin: 0.3em 0px 0.2em; padding: 0px;">
<span font-size:="" small="">Fix it by adjusting the b-vector table:</span></h3>
- In the bvecs text file, swap the x and y row.<br />
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- And in this specific case: <b>fsl2scheme -bvecfile bvecs_yx.txt -bvalfile bvals.txt -usegradmod prefix.scheme</b>
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Remember:</div>
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<span style="color: #494949;">x - </span><span style="color: red;">Red</span><br />
<span style="color: #494949;">y - </span><span style="color: lime;">Green</span><br />
<span style="color: #494949;">z - </span><span style="color: blue;">Blue</span> <br />
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-18305929109059617152014-03-04T13:43:00.000-06:002015-10-09T00:10:34.367-05:00DTI Quality Control - Part 1: Acquisition<div>
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A major issue in DTI image analysis is quality control. Effective quality control for diffusion images relies both on pre tensor fitting (the diffusion weighted images; DWI), and post tensor fitting (the diffusion tensor images; DTI) checks.</div>
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First you will have to check if all volumes were acquired correctly; using fslinfo is one method:</div>
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<a href="http://4.bp.blogspot.com/-hUiCjTQichQ/UjHH66680MI/AAAAAAAABf4/JUEmXnBt6b8/s1600/scan_info.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="255" src="http://4.bp.blogspot.com/-hUiCjTQichQ/UjHH66680MI/AAAAAAAABf4/JUEmXnBt6b8/s400/scan_info.png" width="400" /></a></div>
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<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;"><br />
</span> <span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">This way you can check if each image has the correct number of volumes and the correct pixel dimensions. Something else you should really pay attention to is the quality of each raw DWI image. If you visually scroll through all the different directions you might come across this:</span></div>
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<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;"><a href="http://2.bp.blogspot.com/-sNTkzNQ1Q3I/UVHGeX5oAhI/AAAAAAAABZQ/up6JnVKtTlA/s1600/Screen+Shot+2013-03-26+at+10.00.21+AM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="285" src="http://2.bp.blogspot.com/-sNTkzNQ1Q3I/UVHGeX5oAhI/AAAAAAAABZQ/up6JnVKtTlA/s400/Screen+Shot+2013-03-26+at+10.00.21+AM.png" width="400" /></a></span></div>
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<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;">This does not necessarily have to be a problem if it happens occasionally, but I can become a problem for the tensor calculation if it occurs more frequently (depending on how many directions you acquired).</span></div>
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<span style="background-color: white; color: #494949; line-height: 20.24639892578125px;"> Next, check the amount of distortion in your image; echo planar imaging (EPI), which is used for diffusion images - are particularly sensitive to stretching in the prefrontal and ventral temporal parts of the brain (depending on the direction of acquisition). </span><span style="color: #494949;">For example:</span></div>
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<img border="0" height="208" src="http://3.bp.blogspot.com/-DyoWxyGvkzU/URVj_WVNkhI/AAAAAAAAF3A/i8CeDb90TEk/s320/b0_pilot.png" style="-webkit-box-shadow: rgba(0, 0, 0, 0.0980392) 1px 1px 5px; border: 1px solid rgb(238, 238, 238); box-shadow: rgba(0, 0, 0, 0.0980392) 1px 1px 5px; padding: 5px; position: relative;" width="320" /></div>
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<span style="background-color: white; color: #494949;"><span style="line-height: 20.24639892578125px;">This can be fixed by acquiring a fieldmap that measures the amount of distortion. Read more about this </span><a href="http://www.diffusion-imaging.com/2012/03/dti-preprocessing-distortion-correction.html" style="line-height: 20.24639892578125px;" target="_blank">here</a><span style="line-height: 20.24639892578125px;"> and </span><a href="http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/fugue/feat_fieldmap.html" style="line-height: 20.24639892578125px;" target="_blank">here</a><span style="line-height: 20.24639892578125px;">. Or by acquiring data in two opposite directions and then calculating the average. <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP" target="_blank">FSL's TOPUP</a> can help out here. </span></span></div>
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<span style="color: #494949; line-height: 20.24639892578125px;">Some other more serious error images that have no fix:</span></div>
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<a href="http://2.bp.blogspot.com/-tW7CyOiAzsk/UxYosADYDvI/AAAAAAAABo0/3WYt3ZsTLnM/s1600/Screen+Shot+2014-03-04+at+1.25.01+PM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="205" src="http://2.bp.blogspot.com/-tW7CyOiAzsk/UxYosADYDvI/AAAAAAAABo0/3WYt3ZsTLnM/s1600/Screen+Shot+2014-03-04+at+1.25.01+PM.png" width="320" /></a></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-34917737683024480312013-10-31T14:48:00.000-05:002015-11-04T12:11:00.573-06:00Fibernavigator: Interactive tractography visualization tool<div class="separator" style="clear: both; text-align: center;">
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<a href="http://scilus.github.io/fibernavigator/" target="_blank"><img border="0" height="104" src="http://3.bp.blogspot.com/-8Ih3IluOzv4/UnKrdZ7XDQI/AAAAAAAABi0/djaaqh8sPzk/s400/Screen+Shot+2013-10-31+at+2.11.31+PM.png" width="640" /></a></div>
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Fibernavigator is an interactive diffusion imaging tractography visualization tool. That, as a bonus, is compatible with DTI data processed by FSL. You can download the open-source software for free from their github website (compatible with windows, mac and linux):</div>
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<a href="http://scilus.github.io/fibernavigator/">http://scilus.github.io/fibernavigator/</a></div>
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Watch their user tutorial here:</div>
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<iframe allowfullscreen="" frameborder="0" height="315" src="//www.youtube.com/embed/8X_eOB9zYU8" width="560"></iframe> </div>
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And their tutorial on real-time tractography of the optic radiation:</div>
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<iframe width="320" height="266" class="YOUTUBE-iframe-video" data-thumbnail-src="https://i.ytimg.com/vi/UDFpvyrXNyA/0.jpg" src="https://www.youtube.com/embed/UDFpvyrXNyA?feature=player_embedded" frameborder="0" allowfullscreen></iframe></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-7926093900766982922013-09-20T18:09:00.002-05:002015-10-09T00:12:19.993-05:00DTI tutorial for FSL<div class="separator" style="clear: both; text-align: center;">
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<a href="http://www.cabiatl.com/CABI/resources/dti-analysis/" target="_blank"><img border="0" height="31" src="http://2.bp.blogspot.com/-xK9f43ge-e0/UjzPT0w02RI/AAAAAAAABgU/kvNz4tQkRuI/s400/Screen+Shot+2013-09-20+at+5.40.04+PM.png" width="400" /></a></div>
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<a href="http://www.cabiatl.com/CABI/resources/dti-analysis/" target="_blank"><img border="0" height="162" src="http://3.bp.blogspot.com/-oZnqYZNU-OQ/UjzPT--9PCI/AAAAAAAABgY/1UgAU7x8WlQ/s400/mFA_mMD.jpg" width="400" /></a></div>
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Check out <a href="http://www.cabiatl.com/CABI/resources/dti-analysis/" target="_blank">this website</a> for a great tutorial of diffusion tensor imaging analysis using FSL. It will give you an overview of how to use the FDT Diffusion tool for diffusion imaging analysis, and how you can use <span style="background-color: white; color: #222222;">MedINRIA (</span><span style="color: #222222;"><a href="http://med.inria.fr/" target="_blank">med.inria.fr</a>)</span><span style="background-color: white; color: #222222;"> to visualize the tracts.</span></div>
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Written by <span style="background-color: white; font-size: 16.363636016845703px;">Leigh Morrow, Paul Morgan and Chris Rorden, you can find their site here:</span></div>
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<a href="http://www.cabiatl.com/CABI/resources/dti-analysis/" target="_blank">www.cabiatl.com/CABI/resources/dti-analysis/</a></div>
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Additionally check out the lecture on DTI developed by FSL <span style="font-family: Times;">for more information on Tract-Based Spatial Statistics</span>:</div>
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<a href="http://brainimaging.waisman.wisc.edu/~tromp/fdt.pdf" target="_blank"><img border="0" height="240" src="http://1.bp.blogspot.com/-tq_zRLAdTMc/UjzVOpnOvYI/AAAAAAAABgw/FsqI5JeXNxo/s320/Screen+Shot+2013-09-20+at+6.06.42+PM.png" width="320" /></a></div>
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<a href="http://brainimaging.waisman.wisc.edu/~tromp/fdt.pdf" target="_blank"><img border="0" height="240" src="http://4.bp.blogspot.com/-Pw-uitQUkLU/UjzVOSgHs8I/AAAAAAAABgs/DSAaMTsAdz0/s320/Screen+Shot+2013-09-20+at+6.07.02+PM.png" width="320" /></a></div>
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<a href="http://brainimaging.waisman.wisc.edu/~tromp/fdt.pdf" target="_blank">fsl.fmrib.ox.ac.uk/fslcourse/lectures/fdt.pdf</a></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-89054952929037862632013-09-11T23:15:00.002-05:002015-10-09T00:14:46.304-05:00Diffusion MRI workshop: Videos now online<div class="separator" style="clear: both; text-align: center;">
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<a href="http://tbi.ci.uchicago.edu/" target="_blank"><img border="0" height="83" src="http://2.bp.blogspot.com/-RryXcWbXjxo/UjE3ntbO8xI/AAAAAAAABfc/KF4jq8vAnXg/s400/Screen+Shot+2013-09-11+at+10.39.52+PM.png" width="400" /></a></div>
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The videos and slides of the 2013 workshop on diffusion imaging in traumatic brain injury are now available online, with links to the suggested reading materials:</div>
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<b><a href="http://tbi.ci.uchicago.edu/diffusion-mri-tbi-workshop">http://tbi.ci.uchicago.edu/diffusion-mri-tbi-workshop</a></b></div>
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You can find a selection of videos that are of general interest to DTI researchers after the break.<br />
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DTI-STUDIO software for image processing</h4>
Susumu Mori, Johns Hopkins Univ. School of Medicine, Baltimore, Maryland (USA) <br />
Presentation Slides (<a href="http://tbiworkshop.ci.uchicago.edu/presentations/8-DTIStudio-Mori.pdf">PDF</a>, <a href="http://tbiworkshop.ci.uchicago.edu/presentations/8-DTIStudio-Mori.pptx">PPT</a>)<br />
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<b>Further Reading:</b><br />
<a href="https://www.dtistudio.org/">DTI Studio</a> </div>
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<iframe height="240" src="http://www.youtube.com/embed/F4ncyQ7a-DU" width="320"></iframe></div>
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CAMINO and DTI-TK advanced diffusion MRI pipeline for traumatic brain injury</h4>
Gary Hui Zhang, Univ. College London (UK) <br />
Presentation Slides (<a href="http://tbiworkshop.ci.uchicago.edu/presentations/9-Camino-Zhang.pdf">PDF</a>, <a href="http://tbiworkshop.ci.uchicago.edu/presentations/9-Camino-Zhang.pptx">PPT</a>)<br />
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<b>Further Reading:</b><br />
<a href="http://cmic.cs.ucl.ac.uk/camino/">Camino</a><br />
<a href="http://cmic.cs.ucl.ac.uk/mig/">UCL Microstructure Imaging Group</a></div>
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<iframe height="240" src="http://www.youtube.com/embed/bFF2o9dX1EQ" width="320"></iframe></div>
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<h4>
MedInria neuroimaging software system and traumatic brain injury </h4>
Oliver Commowick, INRIA VISAGES, Rennes (France) <br />
Presentation Slides (<a href="http://tbiworkshop.ci.uchicago.edu/presentations/10-MedInria-Commowick.pdf">PDF</a>, <a href="http://tbiworkshop.ci.uchicago.edu/presentations/10-MedInria-Commowick.pptx">PPT</a>)<br />
<div class="further_reading">
<b>Further Reading:</b><br />
<a href="http://www-sop.inria.fr/asclepios/software/MedINRIA/">MedInria</a> </div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-88684118380473054042013-06-03T16:19:00.000-05:002015-11-09T14:08:27.348-06:00Guest post invitation<br />
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<img border="0" height="294" src="http://1.bp.blogspot.com/-GxguJFG6Y1I/VQWq_junNZI/AAAAAAAACog/MdQq3rEdEOE/s1600/Guest-Blogging.jpg" width="640" /></div>
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Would you like to write a diffusion-imaging.com post on a method, study or conference that is diffusion imaging related? Diffusion-imaging.com is now inviting guest writers to contribute to this website. Comment below or contact me at <a href="mailto:tromp@wisc.edu">tromp at wisc dot edu</a>.</div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-87592122160537145942013-05-20T16:59:00.000-05:002015-11-09T14:24:28.672-06:00Share your diffusion imaging news<div>
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Would you like to stay up to date on what is new in the field of diffusion imaging? Follow us on social media! We also welcome you to share articles that you find interesting with us there.</div>
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<tr><td style="text-align: center;"><span style="margin-left: auto; margin-right: auto;"><a href="https://www.facebook.com/DiffusionImaging" target="_blank"><img border="0" height="168" src="http://2.bp.blogspot.com/-8zsjvZdeHiw/VQWuBbiKAEI/AAAAAAAACo0/eDw8T4ZVuZk/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.05.30%2BAM.png" width="400" /></a></span></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="https://www.facebook.com/DiffusionImaging" target="_blank">Diffusion-imaging on Facebook</a></td></tr>
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<span style="font-family: "georgia" , "times new roman" , serif;"><a href="https://www.facebook.com/DiffusionImaging">https://www.facebook.com/DiffusionImaging</a></span></div>
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<tr><td style="text-align: center;"><span style="margin-left: auto; margin-right: auto;"><a href="https://groups.google.com/forum/#!forum/diffusion-imaging" target="_blank"><img border="0" height="167" src="http://2.bp.blogspot.com/-klWomK4Q60U/VkD-uUgYdBI/AAAAAAAADhY/8Y0EXkPpiGc/s400/Screen%2BShot%2B2015-11-09%2Bat%2B2.13.39%2BPM.png" width="400" /></a></span></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="https://groups.google.com/forum/#!forum/diffusion-imaging" target="_blank">Diffusion-imaging on google groups</a></td></tr>
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<a href="https://groups.google.com/forum/#!forum/diffusion-imaging">https://groups.google.com/forum/#!forum/diffusion-imaging</a></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><span style="margin-left: auto; margin-right: auto;"><a href="http://www.youtube.com/c/Diffusionimaginggroup" target="_blank"><img border="0" height="315" src="http://3.bp.blogspot.com/--5IFzI-77eE/VkD_iFbc06I/AAAAAAAADhg/5NqJ_5JLbjA/s400/Screen%2BShot%2B2015-11-09%2Bat%2B2.17.40%2BPM.png" width="400" /></a></span></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://www.youtube.com/c/Diffusionimaginggroup" target="_blank">Diffusion-imaging on YouTube</a></td></tr>
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<a href="http://www.youtube.com/c/Diffusionimaginggroup"></a><a href="http://www.youtube.com/c/Diffusionimaginggroup">http://www.youtube.com/c/Diffusionimaginggroup</a></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-82715299459278255752013-03-28T21:29:00.000-05:002015-09-01T15:16:31.821-05:00Human Connectome Project<div class="separator" style="clear: both; text-align: center;"><br />
</div><a href="http://www.humanconnectomeproject.org/" target="_blank"><img border="0" src="http://3.bp.blogspot.com/-cQRImQFw-wY/VQWwNbvovaI/AAAAAAAACpA/O9Ky1sikBSM/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.12.41%2BAM.png" height="140" width="640" /></a><br />
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<div class="separator" style="clear: both; text-align: justified;">The 40 million dollar human connectome project exemplifies the relevance of studying structural and functional brain connectivity. The NIH awarded this money to a consortium of institutions to provide an unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve never before realized conclusions about the living human brain.</div><br />
The human connectome project will in the near future become an amazing source of publicly available data, so keep an eye on that!<br />
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<h3><b>Update:</b></h3>The Quarter 1 data release with 68 subjects is online! It includes structural, functional (resting state and task) and diffusion images. <a href="http://humanconnectome.org/data/">http://humanconnectome.org/data/</a> <br />
<div class="separator" style="clear: both; text-align: center;"><br />
</div><div class="separator" style="clear: both; text-align: center;"><a href="http://3.bp.blogspot.com/-TPhsL2x1GFw/VQWxSn54feI/AAAAAAAACpQ/tzQ5pE60dyE/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.19.56%2BAM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-TPhsL2x1GFw/VQWxSn54feI/AAAAAAAACpQ/tzQ5pE60dyE/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.19.56%2BAM.png" height="200" width="320" /></a></div><div class="separator" style="clear: both;"><br />
</div><h3>More information:</h3><a href="http://www.humanconnectomeproject.org/">http://www.humanconnectomeproject.org/</a> <br />
<a href="http://www.neuroscienceblueprint.nih.gov/connectome/">http://www.neuroscienceblueprint.nih.gov/connectome/</a><br />
<a href="http://humanconnectome.org/">http://humanconnectome.org/</a> <br />
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<a href="http://3.bp.blogspot.com/-Nyv81z2xgmY/VQWwP4lBeVI/AAAAAAAACpI/HuVhIoxSRlw/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.15.16%2BAM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="http://3.bp.blogspot.com/-Nyv81z2xgmY/VQWwP4lBeVI/AAAAAAAACpI/HuVhIoxSRlw/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.15.16%2BAM.png" height="162" width="320" /></a>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-74344913371125382882013-03-26T09:16:00.001-05:002016-10-14T12:45:37.149-05:00DTI Processing - Software Tools<div>
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<span style="font-family: "georgia" , "times new roman" , serif;">When starting out in a new imaging field like diffusion tensor imaging it is easy to be overwhelmed by different processing steps and tools.</span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">A recent publication from <a href="http://journal.frontiersin.org/journal/neuroscience" target="_blank">Frontiers in Neuroscience</a> tries to offer an answer to most of these questions in their appropriately named article: "A hitchhiker's guide to diffusion tensor imaging". You can find the full manuscript <a href="http://www.frontiersin.org/brain_imaging_methods/10.3389/fnins.2013.00031/abstract?utm_source=newsletter&utm_medium=email&utm_campaign=Neuroscience-w12-2013" target="_blank">here</a>.</span></div>
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<div class="separator" style="clear: both; text-align: center;">
<a href="http://www.frontiersin.org/brain_imaging_methods/10.3389/fnins.2013.00031/abstract?utm_source=newsletter&utm_medium=email&utm_campaign=Neuroscience-w12-2013" target="_blank"><img border="0" height="160" src="https://1.bp.blogspot.com/-r5WojsII3_8/VQWzS2gaSBI/AAAAAAAACpc/TjGCw5E2YHQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B11.28.21%2BAM.png" width="640" /></a></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">The article discusses sources of artifacts during acquisition and how to reduce/correct them, they go into some detail on how to do quality control (see more on this <a href="http://www.diffusion-imaging.com/2014/03/dti-quality-control-part-1-acquisition.html" target="_blank">here</a>), what skull stripping methods are available, how you can use RESTORE for robust tensor estimation, etc.</span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">Most importantly, they supply an exhaustive list of DTI software packages that I adapted into the below table.</span></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">DTI tools <o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><b>Pre-processing</b><span style="font-size: 11.0pt; mso-bidi-font-family: "Times New Roman"; mso-fareast-font-family: "Times New Roman";"><o:p></o:p></span></span></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">Tensor estimation<o:p></o:p></span></b></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">Fiber tracking<o:p></o:p></span></b></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">ROI-analysis<o:p></o:p></span></b></div>
</td> <td style="border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid white 1.0pt; border-top: solid white 1.0pt; height: 15.1pt; mso-border-bottom-alt: solid windowtext .5pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; mso-border-top-alt: solid white .5pt; mso-border-top-themecolor: background1; mso-border-top-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<b><span style="font-family: "georgia" , "times new roman" , serif;">Registration<o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.slicer.org/" target="_blank">3D Slicer </a><o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://afni.nimh.nih.gov/afni" target="_blank">AFNI </a><o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.bioimagesuite.org/" target="_blank">BioImage Suite </a><o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.brainvoyager.com/" target="_blank">BrainVoyager QX </a><o:p></o:p></span></div>
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<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 6;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://dipy.org/" target="_blank">Dipy </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 7;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://neuroimage.yonsei.ac.kr/dodti/" target="_blank">DoDTI </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 8;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://graphics.stanford.edu/projects/dti/software/" target="_blank">DTI-Query </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 9;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://dti-tk.sourceforge.net/pmwiki/pmwiki.php" target="_blank">DTI-TK </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 10;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="https://www.mristudio.org/wiki/DtiStudioV2" target="_blank">DTIStudio </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 11;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.exploredti.com/" target="_blank">ExploreDTI </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 12;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://surfer.nmr.mgh.harvard.edu/" target="_blank">Freesurfer </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 13;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT" target="_blank">FSL-FDT </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 14;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS/UserGuide" target="_blank">FSL-TBSS </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 15;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.nitrc.org/projects/jist/" target="_blank">JIST </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 16;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="https://github.com/medInria" target="_blank">MedINRIA </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 17;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://white.stanford.edu/newlm/index.php/MrDiffusion" target="_blank">MrDiffusion </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
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</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 18;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.brain.org.au/software/mrtrix/" target="_blank">MRtrix </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 19;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.lpi.tel.uva.es/saturn/" target="_blank">SATURN </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 20;"> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://www.fil.ion.ucl.ac.uk/spm/ext/" target="_blank">SPM </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 21;"> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="http://trackvis.org/" target="_blank">TrackVis </a><o:p></o:p></span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border: none; height: 15.1pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 22;"> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><a href="https://science.nichd.nih.gov/confluence/display/nihpd/TORTOISE" target="_blank">TORTOISE </a><o:p></o:p></span></div>
</td> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<i><span style="font-family: "georgia" , "times new roman" , serif;">x<o:p></o:p></span></i></div>
</td> <td style="background: #D8D8D8; border-bottom: solid windowtext 1.0pt; border-left: none; border-right: solid white 1.0pt; border-top: none; height: 15.1pt; mso-background-themecolor: background1; mso-background-themeshade: 216; mso-border-bottom-alt: solid windowtext .5pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> </tr>
<tr style="height: 15.1pt; mso-yfti-irow: 23; mso-yfti-lastrow: yes;"> <td style="border: none; height: 15.1pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 95.4pt;" valign="top" width="95"><div class="MsoNormal">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
</td> <td style="border: none; height: 15.1pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 63.0pt;" valign="top" width="63"><div class="MsoNormal" style="text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><b>Pre-processing</b><span style="font-size: 11.0pt; mso-bidi-font-family: "Times New Roman"; mso-fareast-font-family: "Times New Roman";"><o:p></o:p></span></span></div>
</td> <td style="border: none; height: 15.1pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 60.0pt;" valign="top" width="60"><div class="MsoNormal" style="text-align: center;">
<b><span style="font-family: "georgia" , "times new roman" , serif;">Tensor estimation<o:p></o:p></span></b></div>
</td> <td style="border: none; height: 15.1pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 52.5pt;" valign="top" width="53"><div class="MsoNormal" style="text-align: center;">
<b><span style="font-family: "georgia" , "times new roman" , serif;">Fiber tracking<o:p></o:p></span></b></div>
</td> <td style="border: none; height: 15.1pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: .75in;" valign="top" width="54"><div class="MsoNormal" style="text-align: center;">
<b><span style="font-family: "georgia" , "times new roman" , serif;">ROI-analysis<o:p></o:p></span></b></div>
</td> <td style="border-right: solid white 1.0pt; border: none; height: 15.1pt; mso-border-right-alt: solid white .5pt; mso-border-right-themecolor: background1; mso-border-right-themecolor: background1; mso-border-top-alt: solid windowtext .5pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 68.1pt;" valign="top" width="68"><div class="MsoNormal" style="text-align: center;">
<b><span style="font-family: "georgia" , "times new roman" , serif;">Registration<o:p></o:p></span></b></div>
</td> </tr>
</tbody></table>
<div style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
<div style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><i>Reference:</i> J.M. Soares, P. Marques, V. Alves, N. Sousa (2013). <i>A hitchhiker's guide to diffusion tensor imaging,</i> Frontiers in Neuroscience.</span></div>
<div style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br />
</span></div>
<div style="text-align: justify;">
<!--EndFragment--><span style="font-family: "georgia" , "times new roman" , serif;">For my own research I use a combination of packages: FSL for preprocessing, Camino for tensor fitting and fiber tracking, DTI-TK for tensor based normalization and TrackVis for visualization and ROI- analysis.</span><br />
<span style="font-family: "georgia" , "times new roman" , serif;"><br /></span>
<span style="font-family: "georgia" , "times new roman" , serif;">-------------------</span><br />
<span style="font-family: "georgia", "times new roman", serif;">Reference this post as: </span><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">Do Tromp, DTI Processing - Software Tools, </span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; vertical-align: baseline;">The Winnower</em><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; text-align: start;">3:e146057.73165 (2016). DOI:</span><a href="https://dx.doi.org/10.15200/winn.146057.73165" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-align: start; text-decoration: none; vertical-align: baseline;">10.15200/winn.146057.73165</a></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-22695638999951260952013-03-04T13:08:00.002-06:002021-04-03T18:42:58.290-05:00Diffusion imaging discussion forum<div>
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After hosting this diffusion imaging blog for over 4 years I am receiving an increasing amount of questions in my inbox from users. I feel that some of the answers to these questions might benefit more readers and furthermore could benefit from input by other experienced brain imaging researchers. To address this problem I started a diffusion imaging discussion forum. Please feel free to join this group, post questions and add to the discussion. The group is accessible through <a href="https://groups.google.com/forum/#!forum/diffusion-imaging" target="_blank">https://groups.google.com/g/diffusion-imaging</a>.</div>
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Note that a google email address in not necessary to sign up. </div><div style="text-align: justify;"><br /></div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-81203686806242786902013-02-15T09:26:00.000-06:002015-10-09T00:14:21.314-05:00Why Do We Acquire B0 Images in DTI Exams?<br />
This post is in response to the following question that was received from one of our readers: <br />
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"I acquired a DTI exam of a patient, which has an artifact that corrupted the B0 images but left the diffusion encoded images unaffected. Can I scan the patient again a year later and use that B0 image to process both exams?"</blockquote>
To answer this question <a href="http://paradime.net/">Samuel Hurley</a> was kind enough to write a guest blog post:<br />
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Let us begin by observing the difference between a non-diffusion weighted (left) and diffusion-weighted (right) image:<br />
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This image on the left is typically referred to as a "B0 image," although this should not be confused with the variable <i>B0,</i> which describes the the strength of the main magnetic field (e.g. 3T). If we inspect the equation that describes a diffusion weighted imaging (DWI) experiment, we see that the overall level of signal in a <i>DWI</i> image is scaled by a factor <i>S_0</i>: <br />
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<a href="http://4.bp.blogspot.com/-phlRQobeP1I/URJjN-IcPSI/AAAAAAAAF2o/ukzFOCv7LVQ/s1600/DWI_Orig.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-phlRQobeP1I/URJjN-IcPSI/AAAAAAAAF2o/ukzFOCv7LVQ/s1600/DWI_Orig.png" /></a></div>
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In addition to acquiring diffusion-weighted data (b>0, right image), we also need to acquire data with <i style="color: #222222;">b</i>=0:<br />
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<a href="http://4.bp.blogspot.com/-K2dU222gZwI/URVltU4KwkI/AAAAAAAAF3Q/89zfsEq2f6g/s1600/B0_Equation.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="27" src="http://4.bp.blogspot.com/-K2dU222gZwI/URVltU4KwkI/AAAAAAAAF3Q/89zfsEq2f6g/s320/B0_Equation.png" width="320" /></a></div>
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As we observe from the equation, this gives us an image with signal intensity <i style="color: #222222;">S_0.</i> We divide each diffusion weighted image <i>S_DWI</i> by this image in order to remove the <i>S_0</i> scaling term from the equation and properly fit the data to estimate <i style="color: #222222;">ADC</i> (or the diffusion tensor <i style="color: #222222;"><b>D</b></i>, in the case of DTI imaging) <br />
<i>S_0</i>, or the B0 image (as we will refer to it herein), is an image of the anatomy that takes into account tissue signals and contrasts in the absence of diffusion gradients. Because the echo time (<i style="font-size: 12.727272033691406px;">TE</i>) is typically long in a DWI or DTI experiment to accommodate large diffusion encoding gradients, this is typically a <i style="font-size: 12.727272033691406px;">T2</i>-weighted image. However, in addition to this <i style="font-size: 12.727272033691406px;">T2</i> contrast, there are other factors that modulate the intensity of this image.<br />
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One of the main reasons the B0 images are acquired during the same scan as the other DTI encoding directions is due to the scanner's prescan function, which sets the hardware receiver and transmitter gain settings (as well as resonant frequency, shim, and a few other things). These gains determine the way the raw MRI signal in the coil (in volts) is translated into a digitally stored image (as bits and bytes in a file). Each time you run the scan, this gain is re-calibrated, leading to a different signal scale.</div>
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DWI and DTI exams are typically acquired with echo-planar imaging (EPI) readouts in order to reduce the overall duration of the experiment as well prevent phase errors induced by the extremely large diffusion encoding gradients. In spite of advances in magnet shimming and parallel imaging (PI), moderate to severe geometric distortion artifacts are still likely in EPI due to field inhomogeneities (e.g. the signal "pileup" in the frontal lobe [left side] of the above images). Removing and re-positioning a patient will alter how that individual's anatomy interacts with the main field, changing the patterns of geometric distortion. While nonlinear coregistration (e.g. FNIRT, ANTs), phase correction algorithms, and eddy current correction (eddy_correct, FSL) can mitigate some of these distortions, they are not effective at completely removing them. <br />
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Additionally, the sensitivity profile of the coil also modulates the image intensity, and this profile is likely to be different after the patient has been removed and later re-positioned in the magnet. Coil sensitivities are actually removed using image-based parallel imaging (PI) techniques such as SENSE (ASSET, iPAT), as is typically done with a phased array coil in order to reduce EPI geometric distortion artifacts. However, the SNR (<i>g-factor</i>) and residual PI (aliasing) artifacts in different parts of the image will likely be different. Inconsistent SNR levels between B0 images and diffusion weighted images may bias estimates of <i>ADC</i> or <i style="font-weight: bold;">D</i>. <br />
Therefore, the purpose of the B0 images is not only to divide out the baseline <i>T2</i>w signal of tissues, but also to remove these other sources of signal variance listed above. Receiver gains and voltages tend to drift significantly over the timescale of hours; therefore over the course of a year it is unlikely for these values to remain stable. Additionally, by removing and re-placing the patient's head a year later, the coil sensitivities, geometric distortions, and SNR variations due to PI are no longer likely to match up. <br />
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halfSpinDoctorhttp://www.blogger.com/profile/18263052491998933021noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-85712956603864892942013-01-15T17:30:00.002-06:002018-05-23T15:13:06.403-05:00DTI Scalars (FA, MD, AD, RD) - How do they relate to brain structure?<br />
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When working with diffusion tensor images (DTI) it is important to understand what is being measured. If you would like to learn more about how the diffusion tensor relates to FA, MD, AD and RD, you might want to read <a href="http://www.diffusion-imaging.com/2015/10/what-is-diffusion-tensor.html" target="_blank">this post</a>. A different key question that is often posed in this field is how biological microstructure relates to the different measures that are extracted from diffusion images (like FA, MD etc). The table below attempts to clarify how differences and changes in biology influence each measure of diffusivity individually and what pattern of change across measures you might expect.</div>
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UnhideWhenUsed="false" Name="Medium Grid 2 Accent 6"/> <w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 6"/> <w:LsdException Locked="false" Priority="70" SemiHidden="false"
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UnhideWhenUsed="false" Name="Colorful Grid Accent 6"/> <w:LsdException Locked="false" Priority="19" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtle Emphasis"/> <w:LsdException Locked="false" Priority="21" SemiHidden="false"
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UnhideWhenUsed="false" QFormat="true" Name="Book Title"/> <w:LsdException Locked="false" Priority="37" Name="Bibliography"/> <w:LsdException Locked="false" Priority="39" QFormat="true" Name="TOC Heading"/> </w:LatentStyles> </xml><![endif]--> <!--[if gte mso 10]> <style>
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{mso-style-name:"Medium List 1";
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color:black;
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{mso-style-name:"Medium List 1";
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{mso-style-name:"Medium List 1";
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{mso-style-name:"Medium List 1";
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{mso-style-name:"Medium List 1";
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{mso-style-name:"Medium List 1";
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</style> <![endif]--> <!--StartFragment--> </div>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableMediumList1" style="border-collapse: collapse; border: none; mso-border-bottom-alt: solid black 1.0pt; mso-border-bottom-themecolor: text1; mso-border-top-alt: solid black 1.0pt; mso-border-top-themecolor: text1; mso-padding-alt: .7pt 2.9pt .7pt 2.9pt; mso-yfti-tbllook: 1184; width: 100%px;"><tbody>
<tr> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<br /></div>
</td> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<b><span style="font-family: "garamond";">FA<o:p></o:p></span></b></div>
</td> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<b><span style="font-family: "garamond";">MD </span></b><span style="font-family: "garamond"; font-size: 14.0pt;">(</span><span style="color: red;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">1</span></sub></span><span style="font-family: "garamond"; font-size: 14.0pt;">+</span><span style="color: lime;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">2</span></sub></span><span style="font-family: "garamond"; font-size: 14.0pt;">+</span><span style="color: blue;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">3</span></sub></span><span style="font-family: "garamond"; font-size: 14.0pt;">)/3</span><span style="font-family: "garamond";"><o:p></o:p></span></div>
</td> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<b><span style="font-family: "garamond";">AD</span></b><b><span style="font-family: "garamond"; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: major-bidi; mso-fareast-font-family: "MS ゴシック"; mso-fareast-theme-font: major-fareast;"> <o:p></o:p></span></b></div>
<div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<span style="color: red;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">1</span></sub></span><b><span style="color: lime; font-family: "garamond"; font-size: 14.0pt;"><o:p></o:p></span></b></div>
</td> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<b><span style="font-family: "garamond";">RD <o:p></o:p></span></b></div>
<div align="center" class="MsoNormal" style="mso-yfti-cnfc: 1; text-align: center;">
<span style="font-family: "garamond"; font-size: 14.0pt;">(</span><span style="color: lime;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">2</span></sub></span><span style="font-family: "garamond"; font-size: 14.0pt;">+</span><span style="color: blue;"><span style="font-family: "times new roman"; font-size: 14.0pt;">λ</span><sub><span style="font-family: "garamond"; font-size: 14.0pt;">3</span></sub></span><span style="font-family: "garamond"; font-size: 14.0pt;">)/2<b><o:p></o:p></b></span></div>
</td> </tr>
<tr> <td style="background: #F2F2F2; border: none; mso-background-themecolor: background1; mso-background-themeshade: 242; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<br /></div>
</td> <td style="background: #F2F2F2; border: none; mso-background-themecolor: background1; mso-background-themeshade: 242; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal" style="margin-bottom: 12.0pt; mso-layout-grid-align: none; mso-pagination: none; mso-yfti-cnfc: 64; text-autospace: none;">
<span style="font-family: "garamond"; font-size: 10pt;">FA is a summary measure of microstructural integrity. While FA is highly sensitive to microstructural changes, it is less specific to the type of change.<o:p></o:p></span></div>
</td> <td style="background: #F2F2F2; border: none; mso-background-themecolor: background1; mso-background-themeshade: 242; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal" style="margin-bottom: 12.0pt; mso-layout-grid-align: none; mso-pagination: none; mso-yfti-cnfc: 64; text-autospace: none;">
<span style="font-family: "garamond"; font-size: 10pt;">MD is an inverse measure of the membrane density, is very similar for both GM and WM and higher for CSF. MD is sensitive to cellularity, edema, and necrosis.<o:p></o:p></span></div>
</td> <td style="background: #F2F2F2; border: none; mso-background-themecolor: background1; mso-background-themeshade: 242; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond"; font-size: 10pt;">AD tends to be variable in WM changes and pathology. In axonal injury AD decreases. The ADs of WM tracts have been reported to increase with brain maturation.<o:p></o:p></span></div>
</td> <td style="background: #F2F2F2; border: none; mso-background-themecolor: background1; mso-background-themeshade: 242; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal" style="margin-bottom: 12.0pt; mso-layout-grid-align: none; mso-pagination: none; mso-yfti-cnfc: 64; text-autospace: none;">
<span style="font-family: "garamond"; font-size: 10pt;">RD increases in WM with de- or dys-myelination. Changes in the axonal diameters or density may also influence RD.<o:p></o:p></span></div>
</td> </tr>
<tr> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">Gray Matter<o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "wingdings";"><o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> </tr>
<tr> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">White Matter<o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 64; text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "wingdings";"><o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> </tr>
<tr> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">CSF<o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> </tr>
<tr> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">High myelination<o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 64; text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "garamond";"><o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> </tr>
<tr> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">Dense axonal packing<o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "garamond";"><o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> </tr>
<tr> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">WM Maturation<o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> </tr>
<tr> <td style="border: none; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">Axonal degeneration<o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> </tr>
<tr> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">Demyelination<o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="background: silver; border: none; mso-background-themecolor: text1; mso-background-themetint: 63; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="mso-yfti-cnfc: 64; text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "garamond";"><o:p></o:p></span></div>
</td> <td style="background-color: silver; background-position: initial initial; background-repeat: initial initial; border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> </tr>
<tr> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" valign="top" width="20%"><div class="MsoNormal">
<span style="font-family: "garamond";">Low SNR<o:p></o:p></span></div>
</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↑</td> <td style="border: none; padding: 0.7pt 2.9pt; text-align: center; width: 20%;" width="20%">↓</td> <td style="border-bottom: solid black 1.0pt; border: none; mso-border-bottom-themecolor: text1; padding: .7pt 2.9pt .7pt 2.9pt; width: 20.0%;" width="20%"><div align="center" class="MsoNormal" style="text-align: center;">
<span style="font-family: "times new roman";">−</span><span style="font-family: "wingdings";"><o:p></o:p></span></div>
</td> </tr>
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References:</h3>
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Feldman <i>et al</i>. (2010). <i><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4245082/" target="_blank">Diffusion Tensor Imaging: A Review for Pediatric Researchers and Clinicians</a>.</i> J Dev Behav Pediatr.<br />
Alexander <i>et al.</i> (2007). <i><a href="https://www.sciencedirect.com/science/article/pii/S1933721307000955" target="_blank">Diffusion Tensor Imaging of the Brain</a>. </i>Neurotherapeutics.<br />
Alexander <i>et al</i>. (2012). <i><a href="http://online.liebertpub.com/doi/abs/10.1089/brain.2011.0071" target="_blank">Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains</a>. </i>Brain Connectivity.<o:p></o:p><br />
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Definitions:</h3>
AD = Axial Diffusivity<br />
CSF = Cerebral Spinal Fluid<br />
FA = Fractional Anisotropy<br />
GM = Gray Matter<br />
MD = Mean Diffusivity<br />
RD = Radial Diffusivity<br />
SNR = Signal to Noise Ratio<br />
WM = White Matter<br />
λ = Eigen Value; length of the axis in the tensor<br />
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<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-Dyl5V0rBV2U/VQnVvNvmHGI/AAAAAAAACwc/SfqfEf0We_4/s1600/scalars.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="640" src="https://1.bp.blogspot.com/-Dyl5V0rBV2U/VQnVvNvmHGI/AAAAAAAACwc/SfqfEf0We_4/s640/scalars.png" width="396" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Examples of FA, MD, AD and RD maps</td></tr>
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Reference this post as:<span style="border: none; color: #212121; font-family: "georgia" , "times new roman" , "times" , serif; font-size: 12px; list-style: none; margin: 0px; outline: none; padding: 0px;"> </span><span style="color: #212121; font-family: "georgia" , "times new roman" , "times" , serif; font-size: 12px;">Do Tromp, DTI Scalars (FA, MD, AD, RD) - How do they relate to brain structure?, </span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">The Winnower</em><span style="color: #212121; font-family: "georgia" , "times new roman" , "times" , serif; font-size: 12px;">3:e146119.94778 (2016). DOI:</span><a href="https://dx.doi.org/10.15200/winn.146119.94778" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">10.15200/winn.146119.94778</a></div>
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Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-74276479138062618672012-11-18T23:04:00.001-06:002018-01-10T14:00:56.971-06:00DTI Tutorial 3 - Fiber Tractography<div style="text-align: justify;">
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Fiber tractography is a very elegant method that can be used to delineate individual fiber tracts from diffusion images. This tutorial will help you in a step-by-step way trough the process.<br />
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In the first place you will need to produce both a tract-file and scalar-maps like fractional anisotropy (FA) maps and mean diffusivity (MD) maps. You can either simply download the <a href="http://trackvis.org/dtk/" target="_blank">diffusion toolkit</a> which is highly compatible with the visualization program <a href="http://trackvis.org/" target="_blank">TrackVis</a>. Or you can use other tools like the ones from <a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Tutorials.DTI" target="_blank">Camino</a> (you will need this <a href="http://www.nitrc.org/links/?group_id=317&url=https%3A%2F%2Fsourceforge.net%2Fprojects%2Fcamino-trackvis%2Ffiles" target="_blank">camino to trackvis tool</a>) or <a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT" target="_blank">FSL Diffusion Toolbox</a>. You can find more information on distortion correction and preprocessing <a href="http://diffusion-tensor.blogspot.com/2012/06/dti-processing-basics.html" target="_blank">here</a> and <a href="http://diffusion-tensor.blogspot.com/2012/03/dti-preprocessing-distortion-correction.html" target="_blank">here</a>, or you can go through the tutorials on analysis steps <a href="http://www.diffusion-imaging.com/2015/10/dti-tutorial-1-from-scanner-to-tensor.html" target="_blank">here</a> and <a href="http://www.diffusion-imaging.com/2015/10/dti-tutorial-2-normalization-and.html" target="_blank">here</a>. Once you have done that, the hardest part is pretty much over.
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<b>TrackVis</b> by MGH<span style="text-align: start;"> (download TrackVis </span><a href="http://trackvis.org/download/" style="text-align: start;" target="_blank">here</a><span style="text-align: start;">)</span></div>
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<a href="http://trackvis.org/docs/">http://trackvis.org/docs/</a><br />
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Start by downloading the tract visualization program TrackVis. The software is free but you will have to register. TrackVis is compatible with windows, linux and mac. Both the diffusion toolkit and TrackVis are developed by some of the core scientists in diffusion imaging; <a href="https://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=196" target="_blank">Van Wedeen</a> and <a href="http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=278" target="_blank">Ruopeng Wang</a>. The support for the software is great, questions are readily answered trough their <a href="http://trackvis.org/forum/" target="_blank">forum</a> (although currently offline due to spamming, for questions you can email them) and the software is still improving with updates. You will have to keep in mind that this is a three dimensional visualization program so it will demand a fairly decent memory performance of your computer, depending on the size of your files (voxel size, number of tracts). One hint when running whole brain tractrography is to limit the number of tracts traced by applying a white matter mask (simply cutting off any voxels that have FA values below .15 or .2). Another tip is to use the MD map to get rid of much of the noise rim around the brain - by thresholding out the lowest 10%. If you do not have your own data you can <a href="http://brainimaging.waisman.wisc.edu/~tromp/example_data/" target="_blank">download practice date from my website</a> (look for the .trk file [although note that it is about 1GB to download], and the FA, MD, AD and RD files).</div>
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<h2>
1. Load tracts in TrackVis</h2>
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Open TrackVis by clicking the program icon in windows or mac, or through the terminal in linux: <br />
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Example code:<br />
<pre class="prettyprint linenums lang-bsh">trackvis -new tracts.trk &</pre>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-rdysi_RkOFw/UKmtWzMr0gI/AAAAAAAABS4/Y7ayVAaiiaM/s1600/64cca.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="499" src="https://4.bp.blogspot.com/-rdysi_RkOFw/UKmtWzMr0gI/AAAAAAAABS4/Y7ayVAaiiaM/s640/64cca.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">TrackVis GUI</td></tr>
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The panel on the right of the screen is called the <b>control panel, </b>which includes the overview and property section. The lower panel is called the <b>image panel, </b>which includes three orthogonal brain views, an image section and a ROI section. The main image with the 3D rendering is called the <b>render window</b>.</div>
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To load overlay scalar image click this button:</div>
<a href="http://1.bp.blogspot.com/-WRttsqbmBBc/VQW7XFpKjAI/AAAAAAAACqU/Ff-hEpj5q8U/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.01.22%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" height="52" src="https://1.bp.blogspot.com/-WRttsqbmBBc/VQW7XFpKjAI/AAAAAAAACqU/Ff-hEpj5q8U/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.01.22%2BPM.png" width="320" /></a><br />
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Then choose your scalar file e.g.: FA.nii.gz <br />
If you load the scalar but can not see it on the main image play around with Slice Opacity.</div>
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<a href="http://2.bp.blogspot.com/-aXHg_-9-sAc/UKmuJI0qwNI/AAAAAAAABTI/9SKlyEIDAqA/s1600/b11d5.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="250" src="https://2.bp.blogspot.com/-aXHg_-9-sAc/UKmuJI0qwNI/AAAAAAAABTI/9SKlyEIDAqA/s320/b11d5.png" width="320" /></a></div>
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Start to draw ROI's: click: </div>
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<a href="http://1.bp.blogspot.com/-e6Bxe_lFmi4/VQW7XKHEhLI/AAAAAAAACqo/6Imog8Bx-Es/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.09%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="48" src="https://1.bp.blogspot.com/-e6Bxe_lFmi4/VQW7XKHEhLI/AAAAAAAACqo/6Imog8Bx-Es/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.09%2BPM.png" width="320" /></a></div>
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Choose Hand Draw - click on: </div>
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<a href="http://2.bp.blogspot.com/-T5xhUKSmYUY/VQW7XE7RlyI/AAAAAAAACqk/iltye1wjELQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.16%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="32" src="https://2.bp.blogspot.com/-T5xhUKSmYUY/VQW7XE7RlyI/AAAAAAAACqk/iltye1wjELQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.16%2BPM.png" width="320" /></a></div>
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and:</div>
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<a href="http://4.bp.blogspot.com/-Bd5NLNJ7dc8/VQW7XvGNa_I/AAAAAAAACqs/0W8JBxXXHYg/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.57%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="31" src="https://4.bp.blogspot.com/-Bd5NLNJ7dc8/VQW7XvGNa_I/AAAAAAAACqs/0W8JBxXXHYg/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.02.57%2BPM.png" width="320" /></a></div>
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Play around with the tools:</h3>
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<ul style="color: #666666; list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;"><b>- What do the other ROI tools do? </b>More on this on the <a href="http://trackvis.org/docs/?subsect=instructions" style="color: #666666;">TrackVis website</a></li>
<li style="margin-bottom: 0.4em;">- You can only draw ROI's in the image panel and not on the 3D render window. To increase the size of the image panel you can slide it up to make it larger. Double click the render window to hide or show the image panel.</li>
<li style="margin-bottom: 0.4em;">- You can move the main image in both the render window and image panel by clicking and dragging - you can play around with this</li>
<li style="margin-bottom: 0.4em;">- Right click the main image to Reset or Straighten</li>
<li style="margin-bottom: 0.4em;">- Zoom in/out on the orthographic FA images and main image by scrolling - beware this is a slow and sensitive tool.</li>
<li style="margin-bottom: 0.4em;">- You can move the orthographic FA images with shift and click</li>
<li style="margin-bottom: 0.4em;">- You can adjust the orthographic FA images brightness with control and click</li>
<li style="margin-bottom: 0.4em;">- To start selecting specific white matter tracks you can use methods used by:</li>
<ul style="list-style: disc;">
<li style="margin-bottom: 0.4em;"> * Mori et al: <a href="http://www.ncbi.nlm.nih.gov/pubmed/11810663" target="_blank">Imaging Cortical Association Tracts in the Human Brain Using Diffusion-Tensor-Based Axonal Tracking, 2002</a></li>
<li style="margin-bottom: 0.4em;"> * Catani and de Schotten: <a href="http://www.ncbi.nlm.nih.gov/pubmed/18619589" target="_blank">A diffusion tensor imaging tractography atlas for virtual in vivo dissections, 2008</a></li>
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<h2>
2. How to delineate the Uncinate Fasciculus (UNC)</h2>
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<ul style="list-style-image: initial; list-style-position: initial; text-align: start;">
<li style="margin-bottom: 0.4em;">- The delineation of the UNC is contingent on finding the correct coronal slice, specifically the most posterior coronal slice that shows clear separation of the frontal and temporal lobes bilaterally. An example is shown in the image below where each of the four UNC ROI's are shown from the image panel:</li>
</ul>
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<a href="http://4.bp.blogspot.com/-08EVuz4nmzQ/UPXt7VF3dwI/AAAAAAAABWI/_MskHUT5iLw/s1600/tmp.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="77" src="https://4.bp.blogspot.com/-08EVuz4nmzQ/UPXt7VF3dwI/AAAAAAAABWI/_MskHUT5iLw/s400/tmp.png" width="400" /></a></div>
<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em; text-align: left;">- In the right Overview pane in the Objects tab right click Track 1 and select Toggle Existing ROI, choose both left ROI's</li>
</ul>
<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;">- Then right click Track Groups and click New Track Group From Slice, once that is done toggle the two right ROI's for Track 2</li>
<li style="margin-bottom: 0.4em;">- In the Property pane expand the options for Slice Filters - Y, for Operator choose Not. Do this for Track 1 and 2.</li>
</ul>
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<a href="http://2.bp.blogspot.com/-Vxyp0iKLVNk/VQW9E3QRdgI/AAAAAAAACq4/dIJcYtjgxHQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.10.23%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="98" src="https://2.bp.blogspot.com/-Vxyp0iKLVNk/VQW9E3QRdgI/AAAAAAAACq4/dIJcYtjgxHQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.10.23%2BPM.png" width="320" /></a></div>
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<ul style="color: #666666; list-style-image: initial; list-style-position: initial; text-align: start;">
<li style="margin-bottom: 0.4em;">- Next you double click Skip and deselect it, do this for both sides.</li>
<li style="margin-bottom: 0.4em;">- What you see now is roughly all the tracks for the Left and Right Uncinate Fasciculus.</li>
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<a href="http://3.bp.blogspot.com/-Xr7c3bDqf8I/UKm4dwoteDI/AAAAAAAABUI/G2GjgjsEi68/s1600/ef871.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="313" src="https://3.bp.blogspot.com/-Xr7c3bDqf8I/UKm4dwoteDI/AAAAAAAABUI/G2GjgjsEi68/s400/ef871.png" width="400" /></a></div>
<ul style="color: #666666; list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;">- The tracks are not perfect yet, we want to get rid of some of the fibers that belong to other pathways or are just wrong</li>
</ul>
<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;">- Create a new Hand Drawn ROI, choose the rectangle and draw a big rectangle in the middle of the sagittal plane:</li>
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<a href="http://4.bp.blogspot.com/-B44Uqcgt5Bk/VQW9LwDMbkI/AAAAAAAACrA/IoD2ugBVVH0/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.10.53%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="165" src="https://4.bp.blogspot.com/-B44Uqcgt5Bk/VQW9LwDMbkI/AAAAAAAACrA/IoD2ugBVVH0/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.10.53%2BPM.png" width="320" /></a></div>
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<li style="margin-bottom: 0.4em;">- Toggle this fifth ROI to both Track 1 and 2, and in ROI Filters option for ROI 5 choose Operator - Not.</li>
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<a href="http://4.bp.blogspot.com/-QVuor_wFiEk/VQW9SofWZVI/AAAAAAAACrI/o3meoB9KarQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.11.23%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="108" src="https://4.bp.blogspot.com/-QVuor_wFiEk/VQW9SofWZVI/AAAAAAAACrI/o3meoB9KarQ/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.11.23%2BPM.png" width="320" /></a></div>
<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;">- Play around with ROI 5 by adding area's in different planes until you achieve an Uncinate Fasciculus as in the movie below:</li>
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.youtube.com/embed/qv6oKc8YGRw?feature=player_embedded' frameborder='0'></iframe></div>
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<li style="margin-bottom: 0.4em;">- Now that your Right and Left Uncinate Fasciculi are perfect you can look at the stats, you can find those in the Overview pane underneath the Dataset Info tab. Click on More Stats - View</li>
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<a href="http://1.bp.blogspot.com/-DvMdk6-0gXg/UKm5RqrqJYI/AAAAAAAABUg/62tYIr0FXoI/s1600/ce09c.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="238" src="https://1.bp.blogspot.com/-DvMdk6-0gXg/UKm5RqrqJYI/AAAAAAAABUg/62tYIr0FXoI/s320/ce09c.png" width="320" /></a></div>
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<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;"><b> </b>-What is for example the Mean FA of your left Uncinate? And your right? And the whole brain?</li>
</ul>
<ul style="list-style-image: initial; list-style-position: initial;">
<li style="margin-bottom: 0.4em;">- You can export these thats with Export, do this for each side and the whole brain separately.</li>
<li style="margin-bottom: 0.4em;">- As a next step it is smart to start saving everything. Start with saving all the ROI's by right clicking them in the Overview pane, then click on Save, try to think of a good naming convention that can include the UNC and the ROI number. </li>
<li style="margin-bottom: 0.4em;">- Next click File and Save Scene. The newer versions of trackvis seem to deal with this better but if you run into issues with re-opening your scene file try to save the ROI's before you save the scene or it will not load them correctly once you want to re-open the scene.</li>
<li style="margin-bottom: 0.4em;">- Now click Track 1 and 2 and save those as NIfTI files, this gives you the option of opening these files in FSLVIEW and see them in 3D:</li>
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<a href="http://4.bp.blogspot.com/-qrzn4n6IKyI/UKm6eLg4SsI/AAAAAAAABUo/k7uqcTT6mU8/s1600/5fd3f.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="243" src="https://4.bp.blogspot.com/-qrzn4n6IKyI/UKm6eLg4SsI/AAAAAAAABUo/k7uqcTT6mU8/s400/5fd3f.png" width="400" /></a></div>
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<h2>
3. Improve the aesthetics</h2>
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<ul style="list-style-image: initial; list-style-position: initial;"><ul style="list-style: disc;">
<li style="margin-bottom: 0.4em;">- Hide all ROI's</li>
<li style="margin-bottom: 0.4em;">- Hide the Z-plane by unselecting Z in the orthographic view</li>
<li style="margin-bottom: 0.4em;">- In the Property pane find Color Code and click on Directional - choose Scalar. By default the FA map will now be used to color code the track, do this for both sides.</li>
<li style="margin-bottom: 0.4em;">- Adjust the Low and High Threshold for the color coding to be between 0 and 0.5</li>
<li style="margin-bottom: 0.4em;">- You can also adjust the color spectrum, BUT on certain occasions this will crash the program, so use with care.</li>
<li style="margin-bottom: 0.4em;">- Also you can choose to Render a tube instead of a line, try this but keep in mind that rendering this will take a moment.</li>
<li style="margin-bottom: 0.4em;">- The output can then look like this (the overlay in the below images is a T1 image registered to FA):</li>
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<a href="http://2.bp.blogspot.com/-7dKwWZ3Uri8/UKm6zlU1DzI/AAAAAAAABUw/coUp25cwmuA/s1600/171a1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="295" src="https://2.bp.blogspot.com/-7dKwWZ3Uri8/UKm6zlU1DzI/AAAAAAAABUw/coUp25cwmuA/s640/171a1.png" width="400" /></a></div>
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<a href="http://4.bp.blogspot.com/-uDCnlD8Y36E/UKm69xaChBI/AAAAAAAABU4/MCLYoQzDKKY/s1600/9c96e.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="258" src="https://4.bp.blogspot.com/-uDCnlD8Y36E/UKm69xaChBI/AAAAAAAABU4/MCLYoQzDKKY/s640/9c96e.png" width="400" /></a></div>
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<div style="text-align: justify;">
In the image below you can find some of the fiber tracts that are important in autism spectrum disorder (ASD). In green the cingulum, in purple the uncinate fasciculus and in red and blue the arcuate fasciculus and superior longitudinal fasciculus respectively, image from <a href="http://www.ncbi.nlm.nih.gov/pubmed/22786754" target="_blank">Travers et al. 2012</a>.</div>
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<span style="background-color: white; color: #333333; font-family: "open sans"; font-size: 13px; text-align: justify;">Reference this post as:</span><span style="border: none; color: #212121; font-family: "georgia" , "times new roman" , "times" , serif; font-size: 12px; list-style: none; margin: 0px; outline: none; padding: 0px;"> </span><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px;">Do Tromp, DTI Tutorial 3 - Fiber Tractography, </span><em style="border: 0px; box-sizing: border-box; color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">The Winnower</em><span style="color: #212121; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px;">3:e146228.88526 (2016). DOI:</span><a href="https://dx.doi.org/10.15200/winn.146228.88526" style="border: 0px; box-sizing: border-box; font-family: Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: inherit; margin: 0px; padding: 0px; text-decoration: none; vertical-align: baseline;">10.15200/winn.146228.88526</a></div>
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</div>Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.comtag:blogger.com,1999:blog-8989830035345743876.post-29375768393753836872012-11-18T21:17:00.000-06:002015-10-09T00:16:49.571-05:00DTI processing with Camino<div style="text-align: center;">
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<a href="http://4.bp.blogspot.com/-KswF1B1RRQM/VQW_NaI3RWI/AAAAAAAACrU/RhZSmmNL9jM/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.19.03%2BPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="102" src="http://4.bp.blogspot.com/-KswF1B1RRQM/VQW_NaI3RWI/AAAAAAAACrU/RhZSmmNL9jM/s1600/Screen%2BShot%2B2015-03-15%2Bat%2B12.19.03%2BPM.png" width="640" /></a></div>
<br />
UCL has recently added a very comprehensive step-by-step guide to tensor fitting with their Camino software.<br />
From their website:<br />
<blockquote class="tr_bq">
"This tutorial gives an introduction to standard diffusion tensor image fitting with Camino. It gives a step-by-step guide of how to fit the diffusion tensor to data from DTI or HARDI acquisition protocols, how to generate maps of standard markers like mean diffusivity (MD) and fractional anisotropy (FA), and how to generate principal direction and colour FA maps."</blockquote>
<blockquote class="tr_bq">
"In steps 1-7, we use a human data set and go through the steps to reconstruct and visualize the tensor information, then we do similar reconstruction on some animal data. There is some overlap between the two examples, but in the animal data we look at additional details, and show how to fix the scheme file so that the orientation of the tensors is correct in the image space."</blockquote>
<br />
More information here:<br />
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<a href="http://cmic.cs.ucl.ac.uk/camino/index.php?n=Tutorials.DTI" target="_blank"><img border="0" height="265" src="http://4.bp.blogspot.com/-PiuGQSdvp9g/UKmj2XE8BHI/AAAAAAAABSo/MoV5CmSZxrY/s400/Screen+Shot+2012-11-18+at+9.12.36+PM.png" width="400" /></a></div>
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<span style="background-color: white; line-height: 25px; text-align: justify;"><span style="color: #444444; font-family: Garamond, sans-serif, Verdana; font-size: medium;">http://cmic.cs.ucl.ac.uk/camino/index.php?n=Tutorials.DTI</span></span></div>
Do Tromphttp://www.blogger.com/profile/00404295461249275977noreply@blogger.com