dipy_track

Synopsis

Workflow for Local Fiber Tracking.

This workflow use a saved peaks and metrics (PAM) file as input. See [1] and [2] for further details about the method.

Usage

dipy_track [OPTIONS] pam_files stopping_files seeding_files

Input Parameters

• pam_files

  Path to the peaks and metrics files. This path may contain

  wildcards to use multiple masks at once.

• stopping_files

  Path to images (e.g. FA) used for stopping criterion for tracking.

• seeding_files

  A binary image showing where we need to seed for tracking.

General Options

• --use_binary_mask

  If True, uses a binary stopping criterion. If the provided stopping_files are not binary, stopping_thr will be used to binarize the images.

• --stopping_thr

  Threshold applied to stopping volume’s data to identify where tracking has to stop.

• --seed_density

  Number of seeds per dimension inside voxel.

  For example, seed_density of 2 means 8 regularly distributed points in the voxel. And seed density of 1 means 1 point at the center of the voxel.

• --step_size

  Step size (in mm) used for tracking.

• --tracking_method

  Select direction getter strategy :

  • “eudx” (Uses the peaks saved in the pam_files)

  • “deterministic” or “det” for a deterministic tracking (Uses the sh saved in the pam_files, default)

  • “probabilistic” or “prob” for a Probabilistic tracking (Uses the sh saved in the pam_files)

  • “closestpeaks” or “cp” for a ClosestPeaks tracking (Uses the sh saved in the pam_files)

• --pmf_threshold

  Threshold for ODF functions.

• --max_angle

  Maximum angle between streamline segments (range [0, 90]).

• --save_seeds

  If true, save the seeds associated to their streamline in the ‘data_per_streamline’ Tractogram dictionary using ‘seeds’ as the key.

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_tractogram

  Name of the tractogram file to be saved.

References

[1]

Eleftherios Garyfallidis. Towards an accurate brain tractography. PhD thesis, Cambridge University, Cambridge, United Kingdom, 2012. URL:, doi:.

[2]

Bagrat Amirbekian. The Modeling of White Matter Architecture and Networks Using Diffusion MRI: Methods, Tools and Applications. PhD thesis, University of California San Francisco, San Francisco, CA, USA, 2016. URL:, doi:.

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.