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Abstract #3971

Fast Feature-Based Multi-Scale Registration of HARDI Data Using Fourth Order Tensors

Pew-Thian Yap1, Yasheng Chen2, Hongyu An2, John Gilmore3, Weili Lin2, Dinggang Shen2

1Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; 2Department of Radiology, University of North Carolina, Chapel Hill, NC, United States; 3Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States


Fourth Order Tensors (FOTs) give elegant mathematical properties akin to that of the second order tensors. Recent formulation of FOT imposes positivity on the estimates to ensure soundness in a physical sense - a property not often found in more general higher order tensor approximations. Employing FOTs, we propose a fast feature-based multi-scale registration algorithm for whole brain HARDI data. Our registration algorithm requires a low computation cost 5 minutes to register a pair of 128x128x80 images at 2mm isotropic resolution making it practically feasible for clinical applications. Our methods involve three major components: 1) Generation of FOT-based features, 2) Hierarchical correspondence matching, 3) Dense deformation field estimation, and 4) Retransformation.

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