Anisotropy Preserving DTI Processing

Authors:
Anne Collard;Silvère Bonnabel;Christophe Phillips;Rodolphe Sepulchre
Affiliations:
Departement of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium 4000;Robotics lab, Mathématiques et Systèmes, Mines Paris Tech, Paris, France 75006;Departement of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium 4000 and Cyclotron Research Centre, University of Liège, Liège, Belgium 4000;Departement of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium 4000 and Department of Engineering, University of Cambridge, Cambridge, UK CB2 1PZ
Venue:
International Journal of Computer Vision
Year:
2014

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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.