Conformal metric optimization on surface (CMOS) for deformation and mapping in laplace-beltrami embedding space

Authors:
Yonggang Shi;Rongjie Lai;Raja Gill;Daniel Pelletier;David Mohr;Nancy Sicotte;Arthur W. Toga
Affiliations:
Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA;Dept. of Mathematics, University of Southern California, Los Angeles, CA;Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA;Department of Neurology, Yale School of Medicine, New Haven, CT;Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL;Cedar Sinai Medical Center, Los Angeles, CA;Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

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Abstract

In this paper we develop a novel technique for surface deformation and mapping in the high-dimensional Laplace-Beltrami embedding space. The key idea of our work is to realize surface deformation in the embedding space via optimization of a conformal metric on the surface. Numerical techniques are developed for computing derivatives of the eigenvalues and eigenfunctions with respect to the conformal metric, which is then applied to compute surface maps in the embedding space by minimizing an energy function. In our experiments, we demonstrate the robustness of our method by applying it to map hippocampal atrophy of multiple sclerosis patients with depression on a data set of 109 subjects. Statistically significant results have been obtained that show excellent correlation with clinical variables. A comparison with the popular SPHARM tool has also been performed to demonstrate that our method achieves more significant results.