[–pmf_threshold float] [–max_angle float] [–pft_back float] [–pft_front float] [–pft_count int] [–out_dir str] [–out_tractogram str] [–save_seeds] pam_files wm_files gm_files csf_files seeding_files
Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
pam_files Path to the peaks and metrics files. This path may contain wildcards to use multiple masks at once. wm_files Path to white matter partial volume estimate for tracking (CMC). gm_files Path to grey matter partial volume estimate for tracking (CMC). csf_files Path to cerebrospinal fluid partial volume estimate for tracking (CMC). seeding_files A binary image showing where we need to seed for tracking.
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Step size used for tracking (default 0.2mm).
Number of seeds per dimension inside voxel (default 1). For example, seed_density of 2 means 8 regularly distributed points in the voxel. And seed density of 1 means 1 point at the center of the voxel.
Threshold for ODF functions (default 0.1).
Maximum angle between streamline segments (range [0, 90], default 20).
Distance in mm to back track before starting the particle filtering tractography (default 2mm). The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.
Distance in mm to run the particle filtering tractography after the the back track distance (default 1mm). The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.
Number of particles to use in the particle filter (default 15).
If true, save the seeds associated to their streamline in the ‘data_per_streamline’ Tractogram dictionary using ‘seeds’ as the key
Output directory (default input file directory)
Name of the tractogram file to be saved (default ‘tractogram.trk’)
References: Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towardsquantitative connectivity analysis: reducing tractography biases.NeuroImage, 98, 266-278, 2014. 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.