Probabilistic clustering and quantitative analysis of white matter fiber tracts

Authors:
Mahnaz Maddah;William M. Wells, III.;Simon K. Warfield;Carl-Fredrik Westin;W. Eric L. Grimson
Affiliations:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA and Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical ...;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA and Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical ...;Computational Radiology Laboratory, Children’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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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 determined along the fiber pathways. This knowledge is also crucial for any tract-oriented 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 along the trajectories. 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.