Multi-dimensional Parameter Space Exploration for Streamline-specific Tractography

TL;DR

This paper investigates the complex parameter space of streamline-specific tractography, validating a probabilistic method on synthetic data and demonstrating how SSP can reveal insightful patterns in real-world datasets.

Contribution

It introduces the use of streamline-specific parameters to better explore and validate tractography methods, providing new insights into parameter effects.

Findings

Validation of probabilistic tracking with SSP on synthetic data

Insights into parameter space using real-world data

Potential for SSP to improve tractography analysis

Abstract

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.