Generalized Tensor-Based Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors

Abstract--This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling's 2 test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the an...

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Natasha Lepore, Caroline A. Brun, Yi-Yu Chou, Ming-Chang Chiang, Rebecca A. Dutton, Kiralee M. Hayashi, E. Luders, Oscar L. Lopez, Howard Aizenstein, Arthur W. Toga, James T. Becker, Paul M. Thompson
• Keywords: Deformation Tensor, Deformation Tensor Fields, Tensor-based Morphometry, TMI 2008