dipy_bundlewarp [-h] [–dist str] [–alpha float] [–beta int] [–max_iter int] [–affine] [–out_dir str] [–out_linear_moved str] [–out_nonlinear_moved str]

[–out_warp_transform str] [–out_warp_kernel str] [–out_dist str] [–out_matched_pairs str] static_file moving_file

BundleWarp: streamline-based nonlinear registration.

BundleWarp is nonrigid registration method for deformable registration of white matter tracts.

Positional Arguments

static_file Path to the static (reference) .trk file. moving_file Path to the moving (target to be registered) .trk file.

Optional Arguments

-h, --help

show this help message and exit

--dist str

Path to the precalculated distance matrix file.

--alpha float

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. (default 0.3)

--beta int

Represents the strength of the interaction between points Gaussian kernel size. (default 20)

--max_iter int

Maximum number of iterations for deformation process in ml-CPD method. (default 15)


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

Output Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_linear_moved str

Filename of linearly moved bundle.

--out_nonlinear_moved str

Filename of nonlinearly moved (warped) bundle.

--out_warp_transform str

Filename of warp transformations generated by BundleWarp.

--out_warp_kernel str

Filename of regularization gaussian kernel generated by BundleWarp.

--out_dist str

Filename of MDF distance matrix.

--out_matched_pairs str

Filename of matched pairs; treamline correspondences between two bundles.



Chandio et al. “BundleWarp, streamline-based nonlinear registration of white matter tracts.” bioRxiv (2023): 2023-01.

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.