Anisotropy Preserving DTI Processing |
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Authors: | Anne Collard Silvère Bonnabel Christophe Phillips Rodolphe Sepulchre |
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Affiliation: | 1. Departement of Electrical Engineering and Computer Science, University of Liège, 4000, ?Liège, Belgium 2. Robotics lab, Mathématiques et Systèmes, Mines Paris Tech, Boulevard Saint-Michel 60, 75006, ?Paris, France 3. Cyclotron Research Centre, University of Liège, 4000, ?Liège, Belgium 4. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, ?CB2 1PZ, UK
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Abstract: | 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 exploiting a Riemannian framework to address these challenges, the present paper proposes a novel metric and an accompanying computational framework for DTI data processing. The proposed approach grounds the signal processing operations in interpolating curves. Well-chosen interpolating curves are shown to provide a computational framework that is at the same time tractable and information relevant for DTI processing. In addition, and in contrast to earlier methods, it provides an interpolation method which preserves anisotropy, a central information carried by diffusion tensor data. |
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