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Diffusion Tensor Magnetic Resonance Imaging : Brain Connectivity Mapping

Christophe Lenglet 1 Rachid Deriche Olivier Faugeras
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Diffusion tensor MRI probes and quantifies the anisotropic diffusion of water molecules in biological tissues, making it possible to non-invasively infer the architecture of the underlying structures. We introduce a novel approach to the cerebral white matter connectivity mapping from diffusion tensor MRI. We address the problem of consistent neural fibers reconstruction in areas of complex diffusion profiles with potentially multiple fibers orientations. Our method relies on a global modelization of the acquired MRI volume as a Riemannian manifold M and proceeds in 4 majors steps:1. We establish the link between Brownian motion and diffusion MRI by using the Laplace-Beltrami operator on M.2. We then expose how the sole knowledge of the diffusion properties of water molecules on M is sufficient to infer its geometry. There exists a direct mapping between the diffusion tensor and the metric of M.3. Having access to that metric, we propose a novel level set formulation to approximate the distance function related to a radial Brownian motion on M.4. On that basis, a rigorous numerical scheme using the exponential map is derived to estimate the geodesics of M, seen as the diffusion paths of water molecules.Numerical experimentations conducted on synthetic and real diffusion MRI datasets illustrate the potentialities of this global approach.
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Submitted on : Tuesday, May 23, 2006 - 6:06:32 PM
Last modification on : Tuesday, September 22, 2020 - 3:58:30 AM
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  • HAL Id : inria-00071595, version 1



Christophe Lenglet, Rachid Deriche, Olivier Faugeras. Diffusion Tensor Magnetic Resonance Imaging : Brain Connectivity Mapping. RR-4983, INRIA. 2003. ⟨inria-00071595⟩



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