White matter fiber analysis using kernel dictionary learning and sparsity priors
This work addresses the need for efficient analysis of large tractography data in neuroimaging, though it appears incremental as it builds on existing dictionary learning and sparsity methods.
The authors tackled the problem of grouping white matter fiber streamlines from diffusion MRI into meaningful bundles by proposing kernel dictionary learning with sparsity priors, achieving plausible bundle groupings as validated on labeled and Human Connectome Project data.
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.