dipy_recobundles

Synopsis

Recognize bundles

See [1] and [2] for further details about the method.

Usage

dipy_recobundles [OPTIONS] streamline_files model_bundle_files

Input Parameters

• streamline_files

  The path of streamline files where you want to recognize bundles.

• model_bundle_files

  The path of model bundle files.

General Options

• --greater_than

  Keep streamlines that have length greater than this value in mm. (default: 50)

• --less_than

  Keep streamlines have length less than this value in mm. (default: 1000000)

• --no_slr

  Don’t enable local Streamline-based Linear Registration. (default: False)

• --clust_thr

  MDF distance threshold for all streamlines. (default: 15.0)

• --reduction_thr

  Reduce search space by (mm). (default: 15.0)

• --reduction_distance

  Reduction distance type can be mdf or mam. (default: mdf)

• --model_clust_thr

  MDF distance threshold for the model bundles. (default: 2.5)

• --pruning_thr

  Pruning after matching. (default: 8.0)

• --pruning_distance

  Pruning distance type can be mdf or mam. (default: mdf)

• --slr_metric

  Options are None, symmetric, asymmetric or diagonal. (default: symmetric)

• --slr_transform

  Transformation allowed. translation, rigid, similarity or scaling. (default: similarity)

• --slr_matrix

  Options are ‘nano’, ‘tiny’, ‘small’, ‘medium’, ‘large’, ‘huge’. (default: small)

• --refine

  Enable refine recognized bundle. (default: False)

• --r_reduction_thr

  Refine reduce search space by (mm). (default: 12.0)

• --r_pruning_thr

  Refine pruning after matching. (default: 6.0)

• --no_r_slr

  Don’t enable Refine local Streamline-based Linear Registration. (default: False)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_recognized_transf

  Recognized bundle in the space of the model bundle. (default: recognized.trk)

• --out_recognized_labels

  Indices of recognized bundle in the original tractogram. (default: labels.npy)

References

[1]

Eleftherios Garyfallidis, Marc-Alexandre Côté, François Rheault, Jasmeen Sidhu, Janice Hau, Laurent Petit, David Fortin, Stephen Cunanne, and Maxime Descoteaux. Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage, 170:283–295, April 2018. Segmenting the Brain. URL: https://doi.org/10.1016/j.neuroimage.2017.07.015, doi:10.1016/j.neuroimage.2017.07.015.

[2]

Bramsh Qamar Chandio, Shannon Leigh Risacher, Franco Pestilli, Daniel Bullock, Fang-Cheng Yeh, Serge Koudoro, Ariel Rokem, Jaroslaw Harezlak, and Eleftherios Garyfallidis. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Scientific Reports, 10:17149, October 2020. URL: https://doi.org/10.1038/s41598-020-74054-4, doi:10.1038/s41598-020-74054-4.

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.