Tensor-based brain surface modeling and analysis

Authors:
Moo K. Chung;Keith J. Worsley;Steve Robbins;Alan C. Evans
Affiliations:
1Department of Statistics, Department of Biostatistics and Medical Informatics, Keck Laboratory for Functional Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI;2Montreal Neurological Institute, McGill University, Canada;2Montreal Neurological Institute, McGill University, Canada;2Montreal Neurological Institute, McGill University, Canada
Venue:
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Year:
2003

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Abstract

We present a unified computational approach to tensorbased morphometry in detecting the brain surface shape difference between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of the cortex may differ. It is highly likely that such surface shape differences are not uniform over the whole cortex. By computing how such surface metrics differ, the regions of the most rapid structural differences can be localized. To increase the signal to noise ratio, diffusion smoothing based on the explicit estimation of Laplace-Beltrami operator has been developed and applied to the surface metrics. As an illustration, we demonstrate how this new tensor-based surface morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children.