White matter fiber analysis using kernel dictionary learning and sparsity priors

TL;DR

This paper introduces novel kernel dictionary learning and sparsity prior methods for grouping white matter streamlines from diffusion MRI data, improving robustness to overlaps and variations.

Contribution

It presents new frameworks incorporating L-0 norm, group sparsity, and manifold regularization for more accurate streamline clustering.

Findings

Methods effectively group streamlines into plausible bundles.

Sparsity priors enhance clustering performance.

Frameworks handle overlapping bundles and inter-subject variability.

Abstract

Diffusion magnetic resonance imaging, a non-invasive tool to infer white matter fiber connections, produces a large number of streamlines containing a wealth of information on structural connectivity. The size of these tractography outputs makes further analyses complex, creating a need for methods to group streamlines into meaningful bundles. In this work, we address this by proposing a set of kernel dictionary learning and sparsity priors based methods. Proposed frameworks include L-0 norm, group sparsity, as well as manifold regularization prior. The proposed methods allow streamlines to be assigned to more than one bundle, making it more robust to overlapping bundles and inter-subject variations. We evaluate the performance of our method on a labeled set and data from Human Connectome Project. Results highlight the ability of our method to group streamlines into plausible bundles and illustrate the impact of sparsity priors on the performance of the proposed methods.