Streamline-based linear registration.

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


static_files : string moving_files : string x0 : string, optional

rigid, similarity or affine transformation model (default affine)
rm_small_clusters : int, optional
Remove clusters that have less than rm_small_clusters (default 50)
qbx_thr : variable int, optional
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
num_threads : int, optional
Number of threads. If None (default) then all available threads will be used. Only metrics using OpenMP will use this variable.
greater_than : int, optional
Keep streamlines that have length greater than this value (default 50)
less_than : int, optional
Keep streamlines have length less than this value (default 250)
np_pts : int, optional
Number of points for discretizing each streamline (default 20)
progressive : boolean, optional
(default True)
out_dir : string, optional
Output directory (default input file directory)
out_moved : string, optional
Filename of moved tractogram (default ‘moved.trk’)
out_affine : string, optional
Filename of affine for SLR transformation (default ‘affine.txt’)
out_stat_centroids : string, optional
Filename of static centroids (default ‘static_centroids.trk’)
out_moving_centroids : string, optional
Filename of moving centroids (default ‘moving_centroids.trk’)
out_moved_centroids : string, optional
Filename of moved centroids (default ‘moved_centroids.trk’)


The order of operations is the following. First short or long streamlines are removed. Second the tractogram or a random selection of the tractogram is clustered with QuickBundlesX. Then SLR [Garyfallidis15] is applied.


[Garyfallidis15]Garyfallidis et al. “Robust and efficient linear

registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015

[Garyfallidis14]Garyfallidis et al., “Direct native-space fiber

bundle alignment for group comparisons”, ISMRM, 2014.

[Garyfallidis17]Garyfallidis et al. Recognition of white matter

bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.