Approximating anatomical brain connectivity with diffusion tensor MRI using kernel-based diffusion simulations.
We present a new technique for noninvasively tracing brain white matter fiber tracts using diffusion tensor magnetic resonance imaging (DT-MRI). This technique is based on performing 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. 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. The primary advantages of the algorithm lie in the handling of fiber branching and crossing and its seamless adaptation to the platform established by new imaging techniques, such as high angular, q-space, or generalized diffusion tensor imaging.
Zhang
Jun
J
Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA. jzhang@cs.uky.edu
Kang
Ning
N
Rose
Stephen E
SE
D000465:Algorithms; D001185:Artificial Intelligence; D001834:Body Water / Q000378:metabolism; D001921:Brain / Q000166:cytology* / Q000378:metabolism; D003198:Computer Simulation; D004058:Diffusion; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000378:metabolism / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity