HCAIMay 16, 2020

FiberStars: Visual Comparison of Diffusion Tractography Data between Multiple Subjects

arXiv:2005.08090v2
AI Analysis

This tool addresses the need for intuitive and efficient comparison of brain connectivity patterns across subject groups, particularly for researchers studying white matter abnormalities, though it is incremental as it builds on existing visualization methods.

The authors tackled the problem of comparing diffusion tractography data across multiple subjects by developing FiberStars, a visual analysis tool that combines 3D anatomy with 2D visualizations, resulting in users navigating tractography collections faster and more accurately in a user study.

Tractography from high-dimensional diffusion magnetic resonance imaging (dMRI) data allows brain's structural connectivity analysis. Recent dMRI studies aim to compare connectivity patterns across subject groups and disease populations to understand subtle abnormalities in the brain's white matter connectivity and distributions of biologically sensitive dMRI derived metrics. Existing software products focus solely on the anatomy, are not intuitive or restrict the comparison of multiple subjects. In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive visualization of brain fiber clusters combining existing 3D anatomy with compact 2D visualizations. With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers using different views. To evaluate the usability of our software, we performed a quantitative user study. We asked domain experts and non-experts to find patterns in a tractography dataset with either FiberStars or an existing dMRI exploration tool. Our results show that participants using FiberStars can navigate extensive collections of tractography faster and more accurately. All our research, software, and results are available openly.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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