dipy_slr#
Usage#
- dipy_slr [-h] [–x0 str] [–rm_small_clusters int] [–qbx_thr [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
- options:
- -h, --help
show this help message and exit
- --x0 str
rigid, similarity or affine transformation model.
- --rm_small_clusters int
Remove clusters that have less than rm_small_clusters.
–qbx_thr [int …] Thresholds for QuickBundlesX. –num_threads int Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error. Only metrics using OpenMP will use this variable. –greater_than int Keep streamlines that have length greater than this value. –less_than int Keep streamlines have length less than this value. –nb_pts int Number of points for discretizing each streamline. –progressive
Output Arguments(Optional)#
- --out_dir str
Output directory. (default current directory)
- --out_moved str
Filename of moved tractogram.
- --out_affine str
Filename of affine for SLR transformation.
- --out_stat_centroids str
Filename of static centroids.
- --out_moving_centroids str
Filename of moving centroids.
- --out_moved_centroids str
Filename of moved centroids.
References#
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.