Local Water Diffusion Phenomenon Clustering From High Angular Resolution Diffusion Imaging (HARDI)
This work addresses a specific bottleneck in diffusion MRI for neurodegenerative disease research, offering an incremental improvement over existing methods.
The paper tackles the challenge of characterizing complex fiber crossing configurations in brain white matter using high angular resolution diffusion imaging (HARDI), developing an automated identification and classification process for single and crossing fiber bundles, validated on a phantom dataset with ground truth.
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.