MultiFiberTrack

You'll need to adapt some of the commands above for your data (specifically -voxeldims, -bgthresh and -size). You can find the script @@StringZeroPad@@ in camino/SGE. @@convert@@ is an ImageMagick command, which you may need to install.

Once that's all done, you can combine the results into a single file ready for tractography

For my postmortem data set, I had to add @@-trace 8E-10@@, to specify a lower diffusivity for fixed tissue at room temperature than the default, which is meant for in-vivo tissue. I've removed it from the above so in-vivo users can cut and paste. I've used @@-snr 12@@, as the signal to noise of my unweighted images is around 12. Performance is reasonably robust to this setting so don't worry if the SNR varies over the image. Just pick a ball park correct figure.

@@sflutgen -infofile ${DSID}_CalibData.info -outputstem ${DSID}_PAS${REID}_LUT -pdf watson < ${DSID}_CalibDataPAS${REID}_PDs.Bdouble@@

@@sfpeaks -inputmodel sh -order 6 -density 100 -searchradius 1.0 < ${DSID}_QBall.Bdouble > ${DSID}_QBallPDs.Bdouble@@

@@track -inputmodel pds -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_QBall -outputtracts oogl < ${DSID}_QBallPDs.Bdouble@@\\
@@geomview ${DSID}_${SEEDFILEROOT}_QBall1.oogl &@@

@@picopdfs -inputmodel pds -numpds 3 -pdf watson -luts ${DSID}_PAS${REID}_LUT_oneFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble < ${DSID}_QBallPDs.Bdouble > ${DSID}_QBallPDsWatsonPDFs.Bdouble@@\\
@@track -inputmodel pico -pdf watson -numpds 3 -iterations 10 -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_QBall10 -outputtracts oogl < ${DSID}_QBallPDsWatsonPDFs.Bdouble@@\\
@@geomview ${DSID}_QBallPDsWatsonPDFs.Bdouble &@@

@@dtlutgen -schemefile $SCHEMEFILE -trace 8E-10 -snr 16.0 -inversion 1 > ${DSID}_DT_LUT.Bdouble@@\\

Now lets run reduced encoding (RE) PASMRI to recover PAS functions in each voxel. The following variable, @@RE@@, specifies the number of basis functions in the PAS representation. By default, the number of basis functions is the same as the number of gradient directions. The idea of RE-PASMRI is that we can use fewer basis functions, which speeds up the estimation of the PAS. The smaller the number, the quicker the reconstruction, but the more crude the representation. Here we'll try 3 levels of reduced encoding. In practice, you'll need to experiment a bit to pick a setting that recovers crossing fibres reliably and has manageable computation time. @@RE=5@@ is probably too low, but is a good setting to use as a first run to get the whole pipeline working, as PASMRI reconstruction is fast with this low setting. @@RE=16@@ we often find is a good setting with a good trade-off between accuracy and computation time. Worth comparing with a higher setting such as @@RE=30@@ though to see whether any significant improvement occurs.

@@#export RE=30; export REID=30;@@\\
@@#export RE=16; export REID=16;@@\\
@@export RE=5; export REID=05;@@\\

I'm going to run the reconstruction separately for each slice, which is useful for generating nice visualizations of the results using @@sfplot@@. This directory will contain the output for each slice

If you only have one processor, use the following for loop (or just run the whole file in one go):

I happen to have 8, so I ran a separate process on each using the following set of 8 different for loops over the slices:

@@ export SNUM=``StringZeroPad $i 2`@@\\

This tutorial runs through deterministic and probabilistic tractography using multiple-fiber reconstructions like PASMRI and q-ball. I'll start with a data file of diffusion weighted measurements in Camino voxel-order format, called B4069.Bfloat, and corresponding schemefile, called B4069.scheme. The data set has 105 gradient directions with b value 4069s/mm^2 (it actually comes from a fixed brain) and 25 b=0 images. Here are the stages we'll go through:

@@export DSID = 'B4069'@@\\
@@export SCHEMEFILE = 'B4069.scheme1'@@\\

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/DT_DetTracts.png

!!! Q-ball reconstruction and tractography

Do the q-ball reconstruction using spherical harmonic representation up to order 6

@@qballmx -schemefile $SCHEMEFILE -basistype sh -order 6 > ${DSID}_QBALLMX.Bdouble@@\\
@@linrecon $DSID.Bfloat $SCHEMEFILE ${DSID}_QBALLMX.Bdouble -normalize -bgthresh 200 > ${DSID}_QBall.Bdouble@@\\
@@sfpeaks -inputmodel sh -order 6 -density 100 -searchradius 1.0 < ${DSID}_QBall.Bdouble > ${DSID}_QBallPDs.Bdouble@@\\

Note the @@sfpeaks@@ command can take quite a while.

Check the directions are OK:

@@pdview -inputmodel pds -datadims ${XSIZE} ${YSIZE} ${ZSIZE} -scalarfile ${DSID}_FA.img < ${DSID}_QBallPDs.Bdouble@@

Do some deterministic tractography

@@track -inputmodel pds -seedfile ../G140/MidSagCC -outputroot /tmp/G300_QBall6_MidSagCC -outputtracts oogl < ${DSID}_QBallPDs.Bdouble@@\\
@@geomview /tmp/G300_QBall6_MidSagCC1.oogl &@@

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/QBall_DetTracts.png

Using the same calibration data set constructed above for PAS-PICo, calibrate for q-ball PICo

@@linrecon ${DSID}_CalibData.Bfloat $SCHEMEFILE ${DSID}_QBALLMX.Bdouble -normalize > ${DSID}_CalibDataQBall6.Bdouble@@\\
@@sfpeaks -inputmodel sh -order 6 -inputdatatype double -density 100 -searchradius 1.0 < ${DSID}_CalibDataQBall6.Bdouble > ${DSID}_CalibDataQBall6_PDs.Bdouble@@\\
@@sflutgen -infofile ${DSID}_CalibData.info -outputstem ${DSID}_QBall6_LUT -pdf watson < ${DSID}_CalibDataQBall6_PDs.Bdouble@@

Get the pdfs and run the tractography

@@picopdfs -inputmodel pds -numpds 3 -pdf watson -luts ${DSID}_PAS${REID}_LUT_oneFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble < PAS${REID}_${DSID}/PDs.Bdouble > PAS${REID}_${DSID}/PDsWatsonPDFs.Bdouble@@\\
@@track -inputmodel pico -pdf watson -numpds 3 -iterations 10 -seedfile ../G140/MidSagCC -outputroot /tmp/G300_PAS${REID}_MidSagCC_Watson10 -outputtracts oogl < PAS${REID}_${DSID}/PDsWatsonPDFs.Bdouble@@\\
@@geomview /tmp/G300_PAS${REID}_MidSagCC_Watson101.oogl &@@

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/QBall_ProbTracts.png

Here are results for RE=5, 16 and 30

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS05_DetTracts.png http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS16_DetTracts.png http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS30_DetTracts.png

Results for R=5, 16 and 30 as before:

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS05_ProbTracts.png http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS16_ProbTracts.png http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS30_ProbTracts.png

For comparison here's what I get for R=16 and R=30

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS16_DetTracts.png http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS30_DetTracts.png \\

@@track -inputmodel pico -pdf watson -numpds 3 -iterations 10 -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_PAS${REID}10 -outputtracts oogl < PAS${REID}_${DSID}/PDsWatsonPDFs.Bdouble@@\\

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS05_ProbTracts.png \\

@@track -inputmodel dt -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_DT -outputtracts oogl < /tmp/${DSID}_DT.Bdouble@@

@@geomview ${DSID}_${SEEDFILEROOT}_DT1.oogl & @@

@@track -inputmodel pico -seedfile $SEEDFILEROOT -iterations 10 -outputroot ${DSID}_${SEEDFILEROOT}_DT10 -outputtracts oogl < ${DSID}_DT_PDFs.Bdouble@@

@@geomview ${DSID}_${SEEDFILEROOT}_DT101.oogl & @@

!!! PAS Tractography

Deterministic tractography:

@@track -inputmodel pds -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_PAS${REID} -outputtracts oogl < PAS${REID}_${DSID}/PDs.Bdouble@@

For comparison with DT results

@@geomview ${DSID}_${SEEDFILEROOT}_PAS${REID}1.oogl & @@

Here are results for RE=5

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/PAS05_DetTracts.png \\

Now try some probabilistic tractography. Start by calibrating the pdf generator. This command creates a full size calibration data set (see sfpicocalibdata man page)

@@sfpicocalibdata -schemefile $SCHEMEFILE -snr 12 -infooutputfile ${DSID}_CalibData.info > ${DSID}_CalibData.Bfloat@@

I've used -snr 12, as the signal to noise of my unweighted images is around 12. Performance is reasonably robust to this setting so don't worry if the SNR varies over the image. Just pick a ball park correct figure.

Now do the actual calibration. This involves running RE-PAS on the calibration data set and constructing a look-up table. Over the full calibration data set, even RE-PAS can take a while. To get a quick set of results for a first pass, have a look at the @@sfpicocalibdata@@ man page to see how you can reduce the size of the data set. You may find that you need to add arguments like @@-binincsize 2 -minvectsperbin 10@@ to @@sflutgen@@ below to get it to work on smaller calibration data sets; check the @@sflutgen@@ man page to see what they do. If you find you need such arguments for the full calibration data set, the calibration data set is not large enough. Anyway, here goes...

@@mesd -schemefile $SCHEMEFILE -filter PAS 1.4 -fastmesd -mepointset $RE < ${DSID}_CalibData.Bfloat > ${DSID}_CalibDataPAS${REID}.Bdouble@@\\
@@sfpeaks -schemefile $SCHEMEFILE -inputmodel maxent -filter PAS 1.4 -mepointset $RE -inputdatatype double < ${DSID}_CalibDataPAS${REID}.Bdouble > ${DSID}_CalibDataPAS${REID}_PDs.Bdouble@@\\
@@sflutgen -infofile ${DSID}_CalibData.info -outputstem ${DSID}_PAS${REID}_LUT -pdf watson < ${DSID}_CalibDataPAS${REID}_PDs.Bdouble@@\\

Note that the above uses a Watson model for the pdfs. You can also use Bingham; see @@sflutgen@@ man page.

Now we can get the pdfs and do the tracking:

@@picopdfs -inputmodel pds -numpds 3 -pdf watson -luts ${DSID}_PAS${REID}_LUT_oneFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble ${DSID}_PAS${REID}_LUT_twoFibreLineCoeffs.Bdouble < PAS${REID}_${DSID}/PDs.Bdouble > PAS${REID}_${DSID}/PDsWatsonPDFs.Bdouble@@\\
@@track -inputmodel pico -pdf watson -numpds 3 -iterations 10 -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT}_PAS${REID}10 -outputtracts oogl < PAS${REID}_${DSID}/PDsWatsonPDFs.Bdouble@@
@@geomview ${DSID}_${SEEDFILEROOT}_PAS${REID}101.oogl &@@

@@for ((i=1; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=2; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=3; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=4; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=5; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=6; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=7; i<=${ZSIZE}; i=i+8)); do@@\\
@@#for ((i=8; i<=${ZSIZE}; i=i+8)); do@@\\

@@ echo $i@@\\
@@ export SNUM=`StringZeroPad $i 2`@@\\
@@ shredder $((${COMPONENTS}*${XSIZE}*${YSIZE}*(i-1)*4)) $((${COMPONENTS}*${XSIZE}*${YSIZE}*4)) $((${COMPONENTS}*${XSIZE}*${YSIZE}*${ZSIZE}*4)) < ${DSID}.Bfloat > /tmp/${DSID}_Slice${SNUM}.Bfloat@@\\
@@ dtfit /tmp/${DSID}_Slice${SNUM}.Bfloat $SCHEMEFILE | fa -outputdatatype float > /tmp/${DSID}_Slice${SNUM}_FA.img@@\\
@@ analyzeheader -datadims ${XSIZE} ${YSIZE} 1 -voxeldims 0.5 0.5 0.5 -datatype float > /tmp/${DSID}_Slice${SNUM}_FA.hdr@@\\
@@ mesd -schemefile $SCHEMEFILE -filter PAS 1.4 -fastmesd -mepointset $RE -bgthresh 200 < /tmp/${DSID}_Slice${SNUM}.Bfloat > PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble@@\\
@@ sfpeaks -schemefile $SCHEMEFILE -inputmodel maxent -filter PAS 1.4 -mepointset $RE -inputdatatype double < PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble > PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}_PDs.Bdouble@@\\
@@ sfplot -xsize ${YSIZE} -ysize ${XSIZE} -inputmodel maxent -filter PAS 1.4 -mepointset $RE -pointset 2 -minifigsize 30 30 -minifigseparation 2 2 -inputdatatype double -dircolcode -projection 1 2 < PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble > /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb@@\\
@@ convert -size 4096x4512 -depth 8 /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.png@@\\
@@ rm /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb /tmp/${DSID}_Slice${SNUM}.Bfloat /tmp/${DSID}_Slice${SNUM}_FA.hdr /tmp/${DSID}_Slice${SNUM}_FA.img@@\\

@@done@@

This tutorial runs through deterministic and probabilistic tractography using multiple-fiber reconstructions like PASMRI and q-ball. I'll start with a data file of diffusion weighted measurements in Camino voxel-order format, called B0469.Bfloat, and corresponding schemefile, called B0469.scheme. The data set has 105 gradient directions and 25 b=0 images. Here are the stages we'll go through:

@@export COMPONENTS=130@@\\

!!! Reduced-encoding PASMRI

Now lets run reduced encoding (RE) PASMRI to recover PAS functions in each voxel. The following variable, @@RE@@, specifies the number of basis functions in the PAS representation. By default, it is the same as the number of gradient directions. The idea of RE-PASMRI is that we can use fewer basis functions, which speeds up the estimation of the PAS. The smaller the number, the quicker the reconstruction, but the more crude the representation. Here we'll try 3 levels of reduced encoding. In practice, you'll need to experiment a bit to pick a setting that recovers crossing fibres reliably and has manageable computation time. @@RE=5@@ is probably too low, but is a good setting to use as a first run to get the whole pipeline working, as PASMRI reconstruction is fast with this low setting. @@RE=16@@ we often find is a good setting with a good trade-off between accuracy and computation time. Worth comparing with a higher setting such as @@RE=30@@ though to see whether any significant improvement occurs.

@@#export RE=30; export REID=30;@@
@@#export RE=16; export REID=16;@@
@@export RE=5; export REID=05;@@

This directory will contain the output for each slice

@@mkdir PAS${REID}_${DSID}@@

If you only have one processor, use the following for loop:

@@for ((i=1; i<=${ZSIZE}; i=i+1)); do@@

I happen to have 8, so I ran a separate process on each as follows:

@@for ((i=1; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=2; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=3; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=4; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=5; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=6; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=7; i<=${ZSIZE}; i=i+8)); do@@
@@#for ((i=8; i<=${ZSIZE}; i=i+8)); do@@

@@ echo $i@@

@@ export SNUM=`StringZeroPad $i 2`@@
@@ shredder $((${COMPONENTS}*${XSIZE}*${YSIZE}*(i-1)*4)) $((${COMPONENTS}*${XSIZE}*${YSIZE}*4)) $((${COMPONENTS}*${XSIZE}*${YSIZE}*${ZSIZE}*4)) < ${DSID}.Bfloat > /tmp/${DSID}_Slice${SNUM}.Bfloat@@
@@ dtfit /tmp/${DSID}_Slice${SNUM}.Bfloat $SCHEMEFILE | fa -outputdatatype float > /tmp/${DSID}_Slice${SNUM}_FA.img@@
@@ analyzeheader -datadims ${XSIZE} ${YSIZE} 1 -voxeldims 0.5 0.5 0.5 -datatype float > /tmp/${DSID}_Slice${SNUM}_FA.hdr@@

@@ mesd -schemefile $SCHEMEFILE -filter PAS 1.4 -fastmesd -mepointset $RE -bgthresh 200 < /tmp/${DSID}_Slice${SNUM}.Bfloat > PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble@@
@@ sfpeaks -schemefile $SCHEMEFILE -inputmodel maxent -filter PAS 1.4 -mepointset $RE -inputdatatype double < PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble > PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}_PDs.Bdouble@@

@@ sfplot -xsize ${YSIZE} -ysize ${XSIZE} -inputmodel maxent -filter PAS 1.4 -mepointset $RE -pointset 2 -minifigsize 30 30 -minifigseparation 2 2 -inputdatatype double -dircolcode -projection 1 2 < PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.Bdouble > /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb@@
@@ convert -size 4096x4512 -depth 8 /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb PAS${REID}_${DSID}/${DSID}_Slice${SNUM}_PAS${REID}.png@@

@@ rm /tmp/${DSID}_Slice${SNUM}_PAS${REID}.rgb /tmp/${DSID}_Slice${SNUM}.Bfloat /tmp/${DSID}_Slice${SNUM}_FA.hdr /tmp/${DSID}_Slice${SNUM}_FA.img@@

@@done

@@cat PAS${REID}_${DSID}/*PAS${REID}_PDs.Bdouble > PAS${REID}_${DSID}/PDs.Bdouble@@

You'll need to adapt some of the commands above for your data (specifically -voxeldims, -bgthresh and -size). You can find the script @@StringZeroPad@@ in camino/SGE.

Now you can check that principal directions of the PAS functions:

@@pdview -inputmodel pds -datadims ${XSIZE} ${YSIZE} ${ZSIZE} -scalarfile ${DSID}_FA.img < PAS${REID}_${DSID}/PDs.Bdouble@@



# Check tractography is working

# This seems to work OK.
track -inputmodel pds -seedfile ../G140/MidSagCC -outputroot /tmp/G300_MidSagCC -outputtracts oogl < PAS${REID}_${DSID}/PDs.Bdouble
geomview /tmp/G300_MidSagCC1.oogl &

* PAS reconstruction using reduced encoding PASMRI (see Sweet and Alexander ISMRM 2010).

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/DT_DetTracts.png \\

http://www.cs.ucl.ac.uk/research/medic/camino/tutorials/files/mfrtrack/DT_ProbTracts.png \\

Define some variables used throughout, which are specific to my data set:\\

@@export DSID = 'B0469'@@\\
@@export SCHEMEFILE = 'B0469.scheme1'@@\\
@@export XSIZE = 128@@\\
@@export YSIZE = 141@@\\
@@export YSIZE = 30@@\\
@@export SEEDFILEROOT = 'MidSagCC'@@\\


!!! DTI Tractography

@@dtfit ${DSID}.Bfloat $SCHEMEFILE > /tmp/${DSID}_DT.Bdouble@@\\
@@dteig < /tmp/${DSID}_DT.Bdouble | pdview -datadims $XSIZE $YSIZE $ZSIZE@@

Run deterministic tractography from a seed region (mine is the midsagittal corpus callosum)

@@track -inputmodel dt -seedfile $SEEDFILEROOT -outputroot ${DSID}_${SEEDFILEROOT} -outputtracts oogl < /tmp/${DSID}_DT.Bdouble@@

Display the tracts

@@geomview /tmp/G300_DT_MidSagCC1.oogl & @@

Here's what we get

Now run some probabilistic tractography. Start by generating the look up table and create the pdfs in each voxel

@@dtlutgen -schemefile $SCHEMEFILE -trace 8E-10 -snr 16.0 -inversion 1 > ${DSID}_DT_LUT.Bdouble@@
@@cat /tmp/${DSID}_DT.Bdouble | picopdfs -inputmodel dt -luts ${DSID}_DT_LUT.Bdouble > ${DSID}_DT_PDFs.Bdouble@@

Now do the tracking (you may want to increase the number of iterations in a real application)

@@track -inputmodel pico -seedfile $SEEDFILEROOT -iterations 10 -outputroot ${DSID}_${SEEDFILEROOT}10 -outputtracts oogl < ${DSID}_DT_PDFs.Bdouble@@

And display the results for comparison

@@geomview ${DSID}_${SEEDFILEROOT}101.oogl & @@

This tutorial runs through deterministic and probabilistic tractography using multiple-fiber reconstructions like PASMRI and q-ball. I'll start with a data file of diffusion weighted measurements in Camino voxel-order format, called X.Bfloat, and corresponding schemefile, called X.scheme. Here are the stages we'll go through:
* Deterministic and probabilistic DTI tractography as a baseline
* PAS reconstruction using reduced encoding PASMRI.
* Deterministic tractography using PASMRI
* Probabilistic tractography using PASMRI
* q-ball reconstruction and tractography for comparison

!! DTI Tractography

This is covered in detail in other tutorials, so I'll keep it brief.

Fit the diffusion tensor and check that the tensor orientations correspond with the image orientation: