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Liu Mengxing edited this page Dec 3, 2022 · 10 revisions

Reproducible Tract Profiles

RTP comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography. The tools take MRI data from the scanner and process them through a series of analysis implemented as Docker containers that are integrated into a modern neuroinformatics platform (Flywheel, Docker, Singularity). The platform guarantees that the entire pipeline can be re-executed, using the same data and computational parameters. We describe the DWI analysis tools that are used to identify the positions of a user-defined number of tracts and their diffusion profiles. The combination of these three components defines a system that transforms raw data into reproducible tract profiles for publication.

Although this repository is only for the tracking part, the whole solution is comprised of three main parts from nifti to tractography (each one implemented in a different container): Anatomical ROI creation, diffusion preprocessing, and tracking/metric derivation pipeline.

Structural preprocessing and ROI creation

This container runs Freesurfer, and a set of other components to create ROIs.

Input: T1 nifti image.

Output: Freesurfer's standard output + a folder with a set of ROIs. Most importantly, it will generate a file called fs.zip that is required as an input to RTP-pipeline. fs.zip can be created in this container, or if it is required (because we want to create the ROIs manually, for example) it can be created locally. The folder should contain a folder called fs/ in the base, and nothing else. In the fs folder there will be two things:

  1. brainmask.nii.gz, aparc+aseg.nii.gz (required) and other optional files.
  2. a folder called ROIs: in this folder all the binary nifti ROIs will be stored.

Main components of the container:

  1. Freesurfer 7.3
  2. Hippocampal and Thalamic segmentation
  3. Neuropythy
  4. Cerebellum atlas
  5. Mori atlas ROIs

RTP-preproc

This container does the dMRI data preprocessing.

Input: T1 from Freesurfer and the raw dMRI nifti images.

Output: Preprocessed and anatomically aligned dMRI images.

Main components:

  1. mrTrix
  2. FSL
  3. ANTs

RTP-pipeline

This container does the tracking, and obtains the metrics and the profiles. Check the How to use section for a description of inputs and outputs.

RTP-pipeline uses parts of these tools (depending on the selected options):

  1. mrTrix
  2. mrVista, mrDiffusion and AFQ
  3. Ensemble Tractography (ET)
  4. LiFE/SIFT2

More documentation

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