dipy_bundlewarp

Synopsis

BundleWarp: streamline-based nonlinear registration.

BundleWarp [1] is a nonrigid registration method for deformable registration of white matter tracts.

Usage

dipy_bundlewarp [OPTIONS] static_file moving_file

Input Parameters

• static_file

  Path to the static (reference) .trk file.

• moving_file

  Path to the moving (target to be registered) .trk file.

General Options

• --dist

  Path to the precalculated distance matrix file.

• --alpha

  Represents the trade-off between regularizing the deformation and having points match very closely. Lower value of alpha means high deformations. It is represented with λ in BundleWarp paper. NOTE: setting alpha<=0.01 will result in highly deformable registration that could extremely modify the original anatomy of the moving bundle.

• --beta

  Represents the strength of the interaction between points Gaussian kernel size.

• --max_iter

  Maximum number of iterations for deformation process in ml-CPD method.

• --affine

  If False, use rigid registration as starting point. (default True)

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_linear_moved

  Filename of linearly moved bundle.

• --out_nonlinear_moved

  Filename of nonlinearly moved (warped) bundle.

• --out_warp_transform

  Filename of warp transformations generated by BundleWarp.

• --out_warp_kernel

  Filename of regularization gaussian kernel generated by BundleWarp.

• --out_dist

  Filename of MDF distance matrix.

• --out_matched_pairs

  Filename of matched pairs; streamline correspondences between two bundles.

References

[1]

Bramsh Qamar Chandio, Emanuele Olivetti, David Romero-Bascones, Jaroslaw Harezlak, and Eleftherios Garyfallidis. BundleWarp, streamline-based nonlinear registration of white matter tracts. bioRxiv, 2023. doi:https://doi.org/10.1101/2023.01.04.522802.

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.