Reconstructing Brain White Matter Pathways with Diffusion Tensor MRI Using Kernel-Based Diffusion Simulations

Abstract

A novel algorithm for approximating anatomical brain connectivity in vivo is presented using diffusion tensor magnetic resonance imaging (DT-MRI). This technique relies on simulating diffusion process within a series of overlapping three dimensional diffusion kernels that cover only a small portion of the human brain volume. The shape of the anisotropic diffusion represented by diffusion fronts is used to estimate the directional organization of the underlying white matter fiber tracts. The proposed algorithm is tested on both simulated and real DT-MRI data. The demonstration shows that the synthetic tracts are accurately replicated, while various examples of white matter fiber pathways can be reconstructed as well, with assigned connectivity indices showing uncertainty. Several features of the algorithm are elucidated by the tracking experiments, including its capability of handling fiber branching and crossing, and robustness to noise. Impact of thresholding settings and the kernel size on performance of the algorithm is also analyzed.