UC Irvine

Spherical Mean technique (SMT) is a novel method of quantifying the diffusion properties of the nerve fibers bundles in the central nervous system. It does this by calculating the spherical mean of the diffusion signal and fitting it to a parametric equation to obtain per voxel diffusion coefficients. We used Expectation - Maximization to obtain Gaussian Mixture Models (GMM) to find distinct clusters in per voxel coefficient space. We found that the diffusion properties of all the white matter fibers were clustered into a single Gaussian distribution in 867 brain volume samples. This implies that the diffusion properties of the white matter fibers are relatively homogeneous. Then, we checked this result by comparing the clusters obtained using GMM with tissue classification outputs obtained by clustering Fractional Anisotropy (obtained using Diffusion Tensor modeling), T1 weighted image intensity and B0 image intensity for 867 brain volume samples; we observed that the specific clusters of per voxel diffusion coefficients obtained using GMM represent specific tissue types (grey matter fibers, white matter fibers, cerebrospinal fluid). Since the parameters derived from SMT represent the physical diffusion properties that are independent of microscopic fiber orientation and the distribution of diffusion coefficients of white matter can be modeled by a single Gaussian distribution, we can conclude that the diffusion properties of all white matter fiber are homogeneous.