Fiber Tracking by Simulating Diffusion Process with
Diffusion Kernels in Human Brain with DT-MRI Data

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

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

Abstract

A novel approach for noninvasively tracing brain white matter fiber tracts is presented using diffusion tensor magnetic resonance imaging (DT-MRI) data. This technique is based on performing anisotropic diffusion simulations over a series of overlapping three dimensional diffusion kernels that cover only a small portion of the human brain volume and are geometrically centered upon selected starting voxels where a seed is placed. The simulations conducted over diffusion kernels are initiated from those starting voxels and are utilized to construct diffusion fronts. The fiber pathways are determined by evaluating the distance and orientation from fronts to their corresponding diffusion seed voxels. Synthetic and real DT-MRI data are employed to demonstrate the tracking scheme. It is shown that the synthetic tracts can be accurately replicated, while several major white matter fiber pathways in the human brain can be reproduced noninvasively as well. Since the diffusion simulation makes use of the entire diffusion tensor data, including both the magnitude and orientation information, the proposed approach enhances robustness and reliability in DT-MRI based fiber reconstruction.


Key words: fiber tractography, anisotropic diffusion simulation, diffusion kernel, diffusion tensor MRI

Mathematics Subject Classification:


Download the compressed postscript file kang5.ps.gz, or the PDF file kang5.pdf.
Technical Report No. 428-05, Department of Computer Science, University of Kentucky, Lexington, KY, 2005.

This research was supported in part by the U.S. National Science Foundation under the grant 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.