Inferring the Localization of White-Matter Tracts using Diffusion Driven Label Fusion

TL;DR

This paper presents a novel diffusion-driven label fusion method that accurately infers affected white-matter pathways in the brain, even with lesions disrupting traditional tractography, by aggregating information from healthy subjects.

Contribution

The paper introduces a new diffusion-based label fusion technique that improves pathway inference in lesioned brains, addressing limitations of existing tractography methods.

Findings

Successfully reconstructs pathways affected by focal lesions

Performs well in simulations and public datasets

Outperforms traditional tractography methods

Abstract

Inferring which pathways are affected by a brain lesion is key for both pre and post-treatment planning. However, many disruptive lesions cause changes in the tissue that interrupt tractography algorithms. In such cases, aggregating information from healthy subjects can provide a solution to inferring the affected pathways. In this paper, we introduce a novel label fusion technique that leverages diffusion information to locate brain pathways. Through simulations and experiments in publicly available data we show that our method is able to correctly reconstruct brain pathways, even if they are affected by a focal lesion.