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 * Create masks of cerebral White Matter (WM), cerebellar WM, ventral DC and brainstem.
 * Map cortical parcellation to volume, growing 2mm into the WM and create a mask of the grown cortex.
 * Dilate the brain segmentation output from freesurfer (aparc+aseg.nii.gz) to make a brain mask.
 * Transform all the necessary labels (Anatomical masks) from Anatomical space to Diffusion space, MNI space and CVS space using the transformation matrices computed in steps 1.2-1.3
 * Transform diffusion brain masks to individual anatomical space, MNI space and CVS space.
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This step performs the following sub-steps:

 * Compute least squares tensor estimation using the anatomical mask created in step 1.4 as the brain mask.
 * Check for scalar outputs of tensor fit.
 * Map scalar outputs of tensor fit to MNI and CVS template.
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 * Compute priors, end ROIs and intial control points from the training set
 * Map initial control points and spread them aroundd to diffusion space (Currently possible only for control points from MNI space)
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 * Runs FSL's bedpostx step for modeling
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trac-all

Index

Contents

  1. Name
  2. Usage
  3. Arguments
    1. Required Arguments
    2. Optional Arguments
    3. Clustered Workflow Directives
      1. Processing Stages/Stepwise Directives
    4. Status and Log files (Optional)
    5. Other Arguments (Optional)
  4. Output Directories and Files
    1. SUBJECT_DIR/SUBJECT_NAME
      1. dlabel
        1. anat

Name

trac-all : Performs reconstruction of white matter pathways using an atlas of underlying anatomy

Usage

trac-all

-[Directive] -c <Configuration File> (Using Configuration File to set analysis options)

For NMR-Center-Users Only

  • Do not submit trac-all as a command in the cluster but run it on the command line. It will submit each subject listed in the Configuration File file as a seperate job to the cluster.

trac-all

-[Directive] -subject subjectname -i dicomfile

For Non-MGH Acquisitions - Please supply the bvals and bvecs in the Configuration File. To see the format of the bvals and bvecs accepted by trac-all, refer to this page

Arguments

Required Arguments

-s subjid

the subject data upon which to operate (If not specified in the Configuration File)

-i DWI file

the full path of the raw Diffusion weighted MR images

-[Directive]

one or more directives (see next)

Optional Arguments

-c dmrirc

Configuration File to set analysis options

Here is how you set up a Configuration File to run Tracula

Clustered Workflow Directives

-prep

(Preprocessing) Process steps 1.1 -1.6 (see below)

-bedp

(Bedpost) Process step 2

-path

(Pathway Reconstruction/Tractography) Process step 3

Processing Stages/Stepwise Directives

(Detailed Explanation for each of these steps is given below)

1.1 Image corrections - Eddy current correction with eddy_correct, B0 field map correction with epidewarp.fsl

-corr

to do this step (Default)

-nocorr

to skip this step

This step performs the following sub-steps:

  • Convert the input Diffusion Dicoms to NIFTI (This is skipped if a NIFTI file is given as the input)
  • Change orientation of the input Diffusion images to match FSL's orientation

  • <Optional> B0 inhomogenity correction (This is done using fsl's fieldmap based EPI unwarping tool epdidewarp.fsl. In order to do this step, B0 field map magnitude dicoms should be specified in the Configuration file)

  • Eddy Current / Simple head motion Correction (Eddy currents within the gradient coils induce distortions which can be corrected by a simple affine registration to a reference volume (low-b volume). This step is done using fsl's eddy_correct)
  • Create a low-b Diffusion brain mask 

trac-all -<no>corr -c dmrirc

1.2 Intra-Subject Registration - Diffusion-to-T1 registration with flirt and/or bbregister.

-intra

to do this step (Default)

-nointra

to skip this step

This step performs the following sub-steps:

  • Change orientation of the T1 anatomical image to match FSL's orientation
  • Compute forward and inverse transformation matrices between the original T1 and re-oriented T1.
  • Compute forward and inverse transformation matrices between T1(Original and re-oriented) and low-b Diffusion image using FLIRT and/or BBREGISTER 

trac-all -<no>intra -c dmrirc

1.3 Inter-Subject Registration - T1-to-template registration using MNI and/or CVS templates.

-inter

to do this step (Default)

-nointer

to skip this step

This step performs the following sub-steps: (You can register to either MNI template or CVS template or both. This option can be set in the configuration file)

For MNI template

  • Compute forward and inverse transformations between T1(Original and re-oriented) and the MNI atlas using FLIRT/BBREGISTER
  • Combine the low-b to T1 transformation (from the intra subject registration step) with the above transformation to get Diffusion to MNI/MNI to Diffusion transformations

For CVS template

  • Compute forward and inverse transformations between T1(Original and re-oriented) and the CVS atlas using a highly non-linear registration called Combined Volumetric and Surface based registration (CVS).
  • Combine the low-b to T1 transformation (from the intra subject registration step) with the above transformation to get Diffusion to CVS/CVS to Diffusion transformations 

trac-all -<no>inter -c dmrirc

1.4 White-matter, cortical and whole-brain masks - Generate masks of the white matter and cortex from FreeSurfer outputs, whole-brain masks from T1 and DWIs.

-masks

to do this step (Default)

-nomasks

to skip this step

  • Create masks of cerebral White Matter (WM), cerebellar WM, ventral DC and brainstem.
  • Map cortical parcellation to volume, growing 2mm into the WM and create a mask of the grown cortex.
  • Dilate the brain segmentation output from freesurfer (aparc+aseg.nii.gz) to make a brain mask.
  • Transform all the necessary labels (Anatomical masks) from Anatomical space to Diffusion space, MNI space and CVS space using the transformation matrices computed in steps 1.2-1.3
  • Transform diffusion brain masks to individual anatomical space, MNI space and CVS space. 

trac-all -<no>masks -c dmrirc

1.5 Tensor fit - Tensor model fitting on DWIs.

-tensor

to do this step (Default)

-notensor

to skip this step

This step performs the following sub-steps:

  • Compute least squares tensor estimation using the anatomical mask created in step 1.4 as the brain mask.
  • Check for scalar outputs of tensor fit.
  • Map scalar outputs of tensor fit to MNI and CVS template. 

trac-all -<no>tensor -c dmrirc

1.6 Pathway priors from atlas to T1 - Combine training data and subject's own data to generate pathway priors.

-prior

to do this step (Default)

-noprior

to skip this step

  • Compute priors, end ROIs and intial control points from the training set
  • Map initial control points and spread them aroundd to diffusion space (Currently possible only for control points from MNI space) 

trac-all -<no>prior -c dmrirc

2. Stick and ball model fitting with bedpost (Cluster highly recommended for this step)

  • Runs FSL's bedpostx step for modeling 

trac-all -bedp -c dmrirc

3. Pathway reconstruction - Perform tractography for a single subject

trac-all -path -c dmrirc

Status and Log files (Optional)

-log file

default is $SUBJECTS_DIR/<your_subjectid>/scripts/trac-all.log

-cmd file

default is $SUBJECTS_DIR/<your_subjectid>/scripts/trac-all.cmd

-noappendlog

start new log and status files instead of appending

Other Arguments (Optional)

-no-isrunning

do not check whether dubjects are currently being processed

-sd subjectsdir

specify subjects dir

-umask umask

set unix file permission mask (default 002)

-grp groupid

check that current group is alpha groupid

-allowcoredump

set coredump limit to unlimited

-debug

generate much more output

dontrun

do everything but execute each command

-onlyversions

print version of each binary and exit

-version

print version of this script and exit

-help

print full contents of help

Output Directories and Files

SUBJECT_DIR/SUBJECT_NAME

dlabel

anat

anat_brain_mask.nii.gz

anat_brain_mask-vent.nii.gz

aparc+aseg.nii.gz

cortex+2mm+bs.nii.gz

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trac-all (last edited 2021-12-08 16:06:06 by AnastasiaYendiki)