2012/10/10 by Anne-Sophie Collard, Anne Collard, Collard, Anne +6
Computer Science · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Differential Geometry (math.DG) #FOS: Computer and information sciences #FOS: Mathematics #MRI in cancer diagnosis #Tensor decomposition and applications #cs.CV #math.DG
paper · pdf · doi:10.48550/arxiv.1210.2826
arxiv created 2012/10/10 · openalex publication_date 2012/10/10 · arxiv updated 2012/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Statistical analysis of Diffusion Tensor Imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies that have shown that a Riemannian framework is appropriate to address these challenges, the present paper proposes a novel metric and an accompanying computational framework for DTI data processing. The proposed metric retains the geometry and the computational tractability of earlier methods grounded in the affine invariant metric. In addition, and in contrast to earlier methods, it provides an interpolation method which preserves anisotropy, a central information carried by diffusion tensor data.