Award Date
12-1-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematical Sciences
First Committee Member
Kaushik Ghosh
Second Committee Member
Malwane Ananda
Third Committee Member
Farhad Shokoohi
Fourth Committee Member
Lung-Chang Chien
Number of Pages
145
Abstract
Accurately estimating neuronal fiber directions is crucial in neuroimaging analysis. The Ball-and-Stick Model (BSM), introduced by Behrens et al. (2003), and widely used in software tools like FSL, remains one of the most popular models for this purpose. BSM analyzes each voxel individually, based solely on its signal information.In this dissertation, we propose modifications to BSM, that incorporate signal information from neighboring voxels in the estimation process, potentially improving the accuracy of the estimates. Additionally, within the estimation process, we introduce two novel proposal distributions to enhance the efficiency of the Markov chain Monte Carlo sampling procedure on the simplex domain. By mapping the simplex domain to a circular manifold and making use of the projected gamma distribution, these proposal distributions avoid restrictions arising from narrow regions of simplex vertices and edges, resulting in improved efficiency of the sampler.
We implement these enhancements in R and use extensive simulations to study the properties of the resulting estimates. The proposed modifications are found to give rise to statistically significant improvement in the accuracy of the estimates, when compared to the traditional BSM.
Keywords
Bayesian; BSM; dMRI; neighbourhood; simplex; SPACE
Disciplines
Statistics and Probability
File Format
File Size
5200 KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Mandal, Anjan, "Using Neighborhood Information to Improve Fiber Direction Estimation from Neuroimaging Data" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5189.
http://dx.doi.org/10.34917/38330401
Rights
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