Dipy/GPUStreamlines Tractography (ODF)
Tag: tractography
Category: Tractography
Not the authoritative implementation
TRXViz provides re-implementations or ports of the methods referenced below. These implementations are NOT the authoritative versions — for the canonical implementations, please use the original software packages. Any differences in behavior or bugs are the responsibility of TRXViz, not the original authors. The presence of a citation here indicates only that TRXViz uses a method derived from that work and that users should credit the original authors; it does not imply that the original authors have reviewed, contributed to, or endorsed TRXViz.
Ports
| Direction | # | Kind |
|---|---|---|
| Input | 0 | ODF Field |
| Input | 1 | Voxel Mask |
| Input | 2 | Tracking Plan |
| Output | 0 | Streamline |
Parameters
| Field | Default |
|---|---|
direction_getter |
"Probabilistic" |
fixel_threshold |
0.10000000149011612 |
max_angle_deg |
60.0 |
max_len_mm |
300.0 |
max_points |
501 |
min_len_mm |
10.0 |
relative_peak_threshold |
0.25 |
rng_seed |
42 |
seeds_per_voxel |
1 |
step_size_mm |
0.5 |
Citations
When you use this op in a published workflow, please credit the original authors whose methods TRXViz re-implements or ports:
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TODO: finalize author list. Gpustreamlines: gpu-accelerated streamline tractography. PLOS Computational Biology, 2025. TODO: confirm canonical title and author list once the record is indexed. doi:10.1371/journal.pcbi.1013323. ↩
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Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan Van Der Walt, Maxime Descoteaux, and Ian Nimmo-Smith. Dipy, a library for the analysis of diffusion mri data. Frontiers in neuroinformatics, 8:8, 2014. doi:10.3389/fninf.2014.00008. ↩