• trac-all

trac-all

Index

Name

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

Usage

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.

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

Optional Arguments

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

Clustered Workflow Directives

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

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.

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.

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.

• 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.

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.

• 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)

Other Arguments (Optional)

Output Directories and Files

SUBJECT_DIR/SUBJECT_NAME

dlabel

anat

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