dipy_track_pft

Usage

dipy_track_pft [-h] [–step_size float] [–seed_density int] [–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.

Positional Arguments

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.

Optional Arguments

-h, --help

show this help message and exit

--step_size float

Step size (in mm) used for tracking.

--seed_density int

Number of seeds per dimension inside voxel. 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.

--pmf_threshold float

Threshold for ODF functions.

--max_angle float

Maximum angle between streamline segments (range [0, 90]).

--pft_back float

Distance in mm to back track before starting the particle filtering tractography. The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.

--pft_front float

Distance in mm to run the particle filtering tractography after the the back track distance. The total particle filtering tractography distance is equal to back_tracking_dist + front_tracking_dist.

--pft_count int

Number of particles to use in the particle filter.

--save_seeds

If true, save the seeds associated to their streamline in the ‘data_per_streamline’ Tractogram dictionary using ‘seeds’ as the key.

Output Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_tractogram str

Name of the tractogram file to be saved.

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.