Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

Abstract

A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence along the fiber pathways determined. This knowledge is also crucial for any tractoriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.

Closed stream lines in uncertain vector fields

“…Uncertainty has been considered in different fields of visualization, like isosurface [Djurcilov et al 2002;Grigoryan and Rheingans 2002;Rhodes et al 2003;Brown 2004] and information visualization [Streit et al 2008]. [Maddah et al 2007;Schultz et al 2007] proposes probabilistic fiber tracking in DT-MRI visualization. This work relates to our approach but considers different data classes.…”

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

“…Of course, the anatomical findings should be strengthen by using larger dataset in the future. In this respect, automatic bundling can be used to produce a complete set of anatomically relevant fiber bundles, as in El Kouby et al (2005); Maddah et al (2007). We need also to set up a more automatic procedure to consistently orient the fibers within a bundle and across subjects, to limit the user intervention.…”

Mean q-Ball Strings Obtained by Constrained Procrustes Analysis with Point Sliding

“…For adequate representation of tracts, these attributes should remain objective, not reflecting, or at worst, reflecting only weakly the effects of deficient image resolution, like partial voluming, that is typically present at tract borders. To satisfy these demands, we put forth the idea of accentuating the information along the tract mean line (similar to [3,4,5]), and appropriately weigh the data away from the mean line. This way we lessen the edge effects while taking into consideration all the data present.…”