Multi-dimensional Parameter Space Exploration for Streamline-specific Tractography
This work addresses a specific problem in neuroimaging for researchers, but it is incremental as it builds on existing probabilistic tracking methods.
The paper tackles the challenge of selecting optimal parameters in tractography by exploring a multi-dimensional parameter space using streamline-specific parameters, demonstrating their potential to reveal patterns and add value to current methods.
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.