dipy_slr [-h] [–x0 str] [–rm_small_clusters int]

[–qbx_thr [int [int …]]] [–num_threads int] [–greater_than int] [–less_than int] [–nb_pts int] [–progressive] [–out_dir str] [–out_moved str] [–out_affine str] [–out_stat_centroids str] [–out_moving_centroids str] [–out_moved_centroids str] static_files moving_files

Streamline-based linear registration.

For efficiency we apply the registration on cluster centroids and remove small clusters.

Positional Arguments

static_files moving_files

Optional Arguments

-h, --help

show this help message and exit

--x0 str

rigid, similarity or affine transformation model (default affine)

--rm_small_clusters int

Remove clusters that have less than rm_small_clusters (default 50)

–qbx_thr [int [int …]]

Thresholds for QuickBundlesX (default [40, 30, 20, 15])

--num_threads int

Number of threads. If None (default) then all available threads will be used. Only metrics using OpenMP will use this variable.

--greater_than int

Keep streamlines that have length greater than this value (default 50)

--less_than int

Keep streamlines have length less than this value (default 250)

--nb_pts int

Number of points for discretizing each streamline (default 20)


(default True)

Output Arguments(Optional)

--out_dir str

Output directory (default input file directory)

--out_moved str

Filename of moved tractogram (default ‘moved.trk’)

--out_affine str

Filename of affine for SLR transformation (default ‘affine.txt’)

--out_stat_centroids str

Filename of static centroids (default ‘static_centroids.trk’)

--out_moving_centroids str

Filename of moving centroids (default ‘moving_centroids.trk’)

--out_moved_centroids str

Filename of moved centroids (default ‘moved_centroids.trk’)



Garyfallidis et al. “Robust and efficient linearregistration of white-matter fascicles in the space ofstreamlines”, NeuroImage, 117, 124–140, 2015


Garyfallidis et al., “Direct native-space fiberbundle alignment for group comparisons”, ISMRM, 2014.


Garyfallidis et al. Recognition of white matterbundles using local and global streamline-based registrationand clustering, NeuroImage, 2017.

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.