Local Water Diffusion Phenomenon Clustering From High Angular Resolution Diffusion Imaging (HARDI)

TL;DR

This paper develops a method using HARDI data to automatically identify and classify local water diffusion patterns, especially in complex fiber crossing regions, aiding brain white matter analysis.

Contribution

It introduces a novel knowledge extraction approach for classifying water diffusion phenomena in HARDI data, improving detection of fiber crossings.

Findings

Validated on a phantom dataset with ground truth

Effective identification of single and crossing fiber voxels

Enhanced understanding of complex fiber configurations

Abstract

The understanding of neurodegenerative diseases undoubtedly passes through the study of human brain white matter fiber tracts. To date, diffusion magnetic resonance imaging (dMRI) is the unique technique to obtain information about the neural architecture of the human brain, thus permitting the study of white matter connections and their integrity. However, a remaining challenge of the dMRI community is to better characterize complex fiber crossing configurations, where diffusion tensor imaging (DTI) is limited but high angular resolution diffusion imaging (HARDI) now brings solutions. This paper investigates the development of both identification and classification process of the local water diffusion phenomenon based on HARDI data to automatically detect imaging voxels where there are single and crossing fiber bundle populations. The technique is based on knowledge extraction processes and is validated on a dMRI phantom dataset with ground truth.