White Matter Fiber Tractography via
Anisotropic Diffusion Simulation in the Human Brain

Ning Kang, Jun Zhang
Laboratory for High Performance Scientific Computing and Computer Simulation
Department of Computer Science
University of Kentucky
773 Anderson Hall
Lexington, KY 40506-0046, USA

Eric S. Carlson
Department of Chemical Engineering
University of Alabama
P. O. Box 870203
Tuscaloosa, AL 35487-0203, USA

Daniel Gembris
Institute for Computational Medicine
University of Mannheim
B6, 23-29C, D-68131 Mannheim, Germany


A novel approach to noninvasively tracing brain white matter fiber tracts is presented using diffusion tensor magnetic resonance imaging (DT-MRI). This technique is based on successive anisotropic diffusion simulations over the human brain, which are utilized to construct three dimensional diffusion fronts. The fiber pathways are determined by evaluating the distance and orientation from the fronts to their corresponding diffusion seeds. Synthetic and real DT-MRI data are employed to demonstrate the tracking scheme. It is shown that the synthetic tracts are accurately replicated, and several major white matter fiber pathways can be reproduced noninvasively, with the tract branching being allowed. Since simulating the diffusion process, which is truly a physical phenomenon reflecting the underlying architecture of cerebral tissues, makes full use of the diffusion tensor data, including both the magnitude and orientation information, the proposed approach is expected to enhance robustness and reliability in white matter fiber reconstruction.

Key words: fiber tractography, anisotropic diffusion simulation, diffusion tensor magnetic resonance imaging

Mathematics Subject Classification:

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This paper has been published in IEEE Transactions on Medical Imaging, Vol. 24, No. 9, pp. 1127-1137, 2005.
The published version is slightly different from the original technical version, which is Technical Report No. 410-04, Department of Computer Science, University of Kentucky, Lexington, KY, 2004.

This research was supported in part by the U.S. National Science Foundation under the grant CCR-9988165, CCR-0092532, and ACR-0202934, in part by the U.S. Department of Energy Office of Science under grant DE-FG02-02ER45961, and in part by the University of Kentucky Research Committee.