Groupwise track filtering via iterative message passing and pruning

Tractography is an important tool for the in vivo analysis of brain connectivity based on diffusion MRI data, but it also has well-known limitations in false positives and negatives for the faithful reconstruction of neuroanatomy. These problems persist even in the presence of strong anatomical priors in the form of multiple region of interests (ROIs) to constrain the trajectories of fiber tractography. In this work, we propose a novel track filtering method by leveraging the groupwise consistency of fiber bundles that naturally exists across subjects. We first formalize our groupwise concept with a flexible definition that characterizes the consistency of a track with respect to other group members based on three important aspects: degree, affinity, and proximity. An iterative algorithm is then developed to dynamically update the localized consistency measure of all streamlines via message passing from a reference set, which then informs the pruning of outlier points from each streamline. In our experiments, we successfully applied our method to diffusion imaging data of varying resolutions from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Human Connectome Project (HCP) for the consistent reconstruction of three important fiber bundles in human brain: the fornix, locus coeruleus pathways, and corticospinal tract. Both qualitative evaluations and quantitative comparisons showed that our method achieved significant improvement in enhancing the anatomical fidelity of fiber bundles.