Brain Connectivity Mapping using Riemannian Geometry, Control Theory and PDEs

Christophe Lenglet 1 Emmanuel Prados 2 Jean-Philippe Pons 3 Rachid Deriche 4 Olivier Faugeras 4
2 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
4 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 : We introduce an original approach for the cerebral white matter connectivity mapping from Diffusion Tensor Imaging (DTI). Our method relies on a global modeling of the acquired Magnetic Resonance Imaging (MRI) volume as a Riemannian manifold whose metric directly derives from the diffusion tensor. These tensors will be used to measure physical three-dimensional distances between different locations of a brain diffusion tensor image. The key concept is the notion of geodesic distance that will allow us to find the optimal paths in the white matter, approximating neural fiber bundles. The geodesic distance function can be seen as the solution of two theoretically equivalent but, in practice, signicantly different problems in the Partial Differential Equation framework: An initial value problem which is is intrinsically dynamic, and a boundary value problem which is, on the contrary, intrinsically stationary. The two approaches have very different properties which make them more or less adequate for our problem and more or less computationally efficient. The dynamic formulation is quite easy to implement but has several practical drawbacks. On the contrary, the stationary formulation is much more tedious to implement, however we will show that it has many virtue which make it more suitable for our connectivity mapping problem. Finally, we will present different possible measures of connectivity, reflecting the degree of connectivity between different regions of the brain. We will illustrate these notions on synthetic and real DTI datasets.
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Christophe Lenglet, Emmanuel Prados, Jean-Philippe Pons, Rachid Deriche, Olivier Faugeras. Brain Connectivity Mapping using Riemannian Geometry, Control Theory and PDEs. SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2009, 2 (2), pp.285-322. ⟨10.1137/070710986⟩. ⟨inria-00377379⟩

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