Control Theory and Fast Marching Techniques for Brain Connectivity Mapping

We propose a novel, fast and robust technique for the computation of anatomical connectivity in the brain. Our approach exploits the information provided by Diffusion Tensor Magnetic Resonance Imaging (or DTI) and models the white matter by using Riemannian geometry and control theory. We show that it is possible, from a region of interest, to compute the geodesic distance to any other point and the associated optimal vector field. The latter can be used to trace shortest paths coinciding with neural fiber bundles. We also demonstrate that no explicit computation of those 3D curves is necessary to assess the degree of connectivity of the region of interest with the rest of the brain. We finally introduce a general local connectivity measure whose statistics along the optimal paths may be used to evaluate the degree of connectivity of any pair of voxels. All those quantities can be computed simultaneously in a Fast Marching framework, directly yielding the connectivity maps. Apart from...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Emmanuel Prados, Stefano Soatto, Christophe Lenglet, Jean-Philippe Pons, Nicolas Wotawa, Rachid Deriche, Olivier D. Faugeras
• Keywords: Anatomical Connectivity, Computer Vision, Connectivity Maps, CVPR 2006, Fast Marching Framework, Geodesic Connectivity, Local Connectivity Measure