Creating Large Scale Population Atlases Using Diffusion Tensor Images
Davatzikos C, Verma R
University of Pennsylvania
We propose a novel framework for the statistical analysis of diffusion tensor images applicable to large population studies, which addresses the challenging problems of tensor statistics, unaddressable by commonly used linear statistical methods, due to the inherent non-linearity of tensors. We perform multivariate statistical analysis on tensors by identifying the underlying manifold of the set of tensors under consideration using the Isomap manifold learning technique and defining geodesic distances between tensors along the manifold. Application on human brains with simulated pathology and average population atlasing, show that the proposed statistical analysis method properly captures statistical relationships among tensor image data, while identifying group differences.