# Multi-dimensional Parameter Space Exploration for Streamline-specific Tractography

arXiv: https://arxiv.org/abs/2408.05056 · HTML: https://mlsanity.com/paper/2408.05056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Combined tract segmentation and orientation mapping for bundle-specific tractography](https://mlsanity.com/paper/1901.10271/) (2019)
- [atTRACTive: Semi-automatic white matter tract segmentation using active learning](https://mlsanity.com/paper/2305.18905/) (2023)

## Abstract (arXiv)

One of the unspoken challenges of tractography is choosing the right parameters for a given dataset or bundle. In order to tackle this challenge, we explore the multi-dimensional parameter space of tractography using streamline-specific parameters (SSP). We 1) validate a state-of-the-art probabilistic tracking method using per-streamline parameters on synthetic data, and 2) show how we can gain insights into the parameter space by focusing on streamline acceptance using real-world data. We demonstrate the potential added value of SSP to the current state of tractography by showing how SSP can be used to reveal patterns in the parameter space.