title: Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging Based on a Riemannian Manifold Approach.
abstract: In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.
authors.forename: Maximilian
authors.initials: M
authors.lastname: Baust
authors.forename: Andreas
authors.initials: A
authors.lastname: Weinmann
authors.forename: Matthias
authors.initials: M
authors.lastname: Wieczorek
authors.forename: Tobias
authors.initials: T
authors.lastname: Lasser
authors.forename: Martin
authors.initials: M
authors.lastname: Storath
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
mesh_terms: D000465:Algorithms; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D021621:Imaging, Three-Dimensional