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.
|Commitee:||Lopour, Beth, Su, Min-Ying|
|School:||University of California, Irvine|
|School Location:||United States -- California|
|Source:||MAI 58/04M(E), Masters Abstracts International|
|Subjects:||Bioengineering, Neurosciences, Medical imaging|
|Keywords:||Clustering, Diffusion tensor imaging, Diffusion weighted imaging, Gaussian mixture modelling, Unsupervised machine learning, White matter|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be