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= # Check registration = == Check registration ==
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= # View FA on the subject's anat: = == View FA on the subject's anat: ==
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= # View FA on fsaverage = == View FA on fsaverage ==
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= # Group/Higher level GLM analysis: =
= # Concatenate fa from individuals into one file =
= # Make sure the order agrees with the fsgd below =
== Group/Higher level GLM analysis: ==
== Concatenate fa from individuals into one file ==
== Make sure the order agrees with the fsgd below ==
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= # Create a mask: = == Create a mask: ==
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= # GLM Fit = == GLM Fit ==




Performs processing of native diffusion tensor imaging (DTI/DWI) data. It takes original dicom images as input, and can automatically detect the bvalue and direction information from certain Siemens sequences. Other users may have to input the bvalue and direction information using bvec and bval text files with the same format as the files used in FSL diffusion processing. The subjectid refers to the subject's cortical reconstruction directory (from Freesurfer/T1 processing). This is used for registration of the diffusion data to the structural images. dt_recon outputs a variety of maps of interest in the study of neural anatomy and integrity as described in more detail below. Output maps can be entered into a variety of region of interest and voxel based analysis procedures (including TBSS from FSL).

If bvalues and bvectors are not specified with --b, it is assumed that the input is a Siemens dicom file, and gets gradient directions and bvalues based on values found in the dicom file. See $FREESURFER_HOME/diffusion/mgh-dti-seqpack/README. If the bvalues and bvectors are specified, then the input volume can be anything.

The bvalues are in a simple text file, one for each direction (including b=0). The bvectors (gradient directions) are also in a simple text file with three components on each row. These also include the b=0 values. There must be as many rows in the bvals/bvecs as there are frames in the input.



--i dti_dicom --s subjectid [Directive]


Required Aruments

--i invol

--b bvals bvecs

--s subjectid

--o outputdir

Other Arguments (Optional)

--ecref TP : Use TP as 0-based reference time points for EC

--no-ec : turn off eddy/motion correction

--no-reg : do not register to subject or resample to talairach

--no-tal : do not resample FA to talairch space

--sd subjectsdir : specify subjects dir (default env SUBJECTS_DIR)

--debug : print out lots of info

--version : print version of this script and exit

--help : voluminous bits of wisdom

Stages and output

1. Convert input to nifti (creates dwi.nii)

2. Eddy current and motion correction using FSLs eddy_correct,

creates dwi-ec.nii. Can take 1-2 hours.

3. DTI GLM Fit and tensor construction. Includes creation of:

tensor.nii -- maps of the tensor (9 frames)

eigvals.nii -- maps of the eigenvalues

eigvec?.nii -- maps of the eigenvectors

adc.nii -- apparent diffusion coefficient

fa.nii -- fractional anisotropy

ra.nii -- relative anisotropy

vr.nii -- volume ratio

ivc.nii -- intervoxel correlation

lowb.nii -- Low B

bvals.dat -- bvalues

bvecs.dat -- directions

Also creates glm-related images:

beta.nii - regression coefficients

eres.nii - residual error (log of dwi intensity)

rvar.nii - residual variance (log)

rstd.nii - residual stddev (log)

dwires.nii - residual error (dwi intensity)

dwirvar.nii - residual variance (dwi intensity)

4. Registration of lowb to same-subject anatomical using

FSLs flirt (creates mask.nii and register.dat)

5. Map FA to talairach space (creates fa-tal.nii)

Example usage:

dt_recon --i 6-1025.dcm --s M87102113 --o dti dt_recon --i f.nii --b f.bvals f.bvecs --s M87102113 --o dti

Check registration

tkregister2 --mov dti/lowb.nii --reg dti/register.dat \

  • --surf orig --tag

View FA on the subject's anat:

tkmedit M87102113 orig.mgz -overlay dti/fa.nii \

  • -overlay-reg dti/register.dat

View FA on fsaverage

tkmedit fsaverage orig.mgz -overlay dti/fa-tal.nii

Group/Higher level GLM analysis:

Concatenate fa from individuals into one file

Make sure the order agrees with the fsgd below

mri_concat */fa-tal.nii --o group-fa-tal.nii

Create a mask:

mri_concat */mask-tal.nii --o group-masksum-tal.nii --mean mri_binarize --i group-masksum-tal.nii --min .999 --o group-mask-tal.nii


mri_glmfit --y group-fa-tal.nii --mask group-mask-tal.nii\

  • --fsgd your.fsgd --C contrast --glmdir groupanadir

dt_recon (last edited 2018-02-05 10:25:28 by MorganFogarty)