Variational frameworks for DT-MRI estimation, regularization and visualization

We address three crucial issues encountered in DT-MRI (diffusion tensor magnetic resonance imaging): diffusion tensor estimation, regularization and fiber bundle visualization. We first review related algorithms existing in the literature and propose then alternative variational formalisms that lead to new and improved schemes, thanks to the preservation of important tensor constraints (positivity, symmetry). We illustrate how our complete DT-MRI processing pipeline can be successfully used to construct and draw fiber bundles in the white matter of the brain, from a set of noisy raw MRl images.