Brain Connectivity Using Geodesics in HARDI

We develop an algorithm for brain connectivity assessment using geodesics in HARDI (high angular resolution diffusion imaging). We propose to recast the problem of finding fibers bundles and connectivity maps to the calculation of shortest paths on a Riemannian manifold defined from fiber ODFs computed from HARDI measurements. Several experiments on real data show that our method is able to segment fibers bundles that are not easily recovered by other existing methods.

[1]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.

[2]  Jeremy D. Schmahmann,et al.  Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers , 2008, NeuroImage.

[3]  Jean-Francois Mangin,et al.  Fiber Tracking in q-Ball Fields Using Regularized Particle Trajectories , 2005, IPMI.

[4]  Guido Gerig,et al.  Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling , 2009, Medical Image Anal..

[5]  Marc Niethammer,et al.  Finsler Tractography for White Matter Connectivity Analysis of the Cingulum Bundle , 2007, MICCAI.

[6]  B W Kreher,et al.  Multitensor approach for analysis and tracking of complex fiber configurations , 2005, Magnetic resonance in medicine.

[7]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[8]  Daniel C Alexander,et al.  Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  Rachid Deriche,et al.  Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions , 2009, IEEE Transactions on Medical Imaging.

[10]  Rachid Deriche,et al.  Brain Connectivity Mapping Using Riemannian Geometry, Control Theory, and PDEs , 2009, SIAM J. Imaging Sci..

[11]  Philip A. Cook,et al.  A general framework for multiple-fibre PICo tractography , 2006 .

[12]  Xavier Bresson,et al.  Representing Diffusion MRI in 5-D Simplifies Regularization and Segmentation of White Matter Tracts , 2007, IEEE Transactions on Medical Imaging.

[13]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods/ J. A. Sethian , 1999 .

[14]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[15]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[16]  Baba C. Vemuri,et al.  A Unified Computational Framework for Deconvolution to Reconstruct Multiple Fibers From Diffusion Weighted MRI , 2007, IEEE Transactions on Medical Imaging.

[17]  Laurent D. Cohen,et al.  Fast extraction of minimal paths in 3D images and applications to virtual endoscopy , 2001, Medical Image Anal..

[18]  Kalvis M. Jansons,et al.  Persistent angular structure: new insights from diffusion magnetic resonance imaging data , 2003 .

[19]  Rachid Deriche,et al.  Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI , 2008, NeuroImage.

[20]  Jean Daunizeau,et al.  Accurate Anisotropic Fast Marching for Diffusion-Based Geodesic Tractography , 2007, Int. J. Biomed. Imaging.

[21]  Carl-Fredrik Westin,et al.  TWO-TENSOR FIBER TRACTOGRAPHY , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.