Tomographic and Morphometric Reconstruction and Analysis With Applications in Material Science and Neuroimaging
Abstract
Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more may be known about the underlying physical process being imaged. In this work, we propose an MBIR framework with a physics based prior model - namely the Cahn-Hilliard equation. The Cahn-Hilliard equation can be used to describe the spatio-temporal evolution of binary alloys. After formulating the MBIR cost with the Cahn-Hilliard prior, we use the Plug-And-Play algorithm with ICD optimization to minimize the resulting cost. We first apply this method to simulated data using the interlaced-view sampling method of data acquisition. Results show superior reconstruction quality compared to the FBP and model-based iterative reconstruction with total variation prior. We next reconstruct a real Al-Cu alloy sample using our method, and compare the results to MBIR with a total variations prior. As a partially related problem, neuroimaging tools are finding increasing use in detecting and analyzing brain changes related with contact sports. About 1.6-3.8 million sports related injuries take place every year, according to an estimate. We investigate the gray matter volume and thickness changes, particularly in the brain regions susceptible to impact, in a population of high school football and soccer athletes. We also correlate functional connectivity changes in soccer athletes with the intensity and length of their sport experience.
Degree
Ph.D.
Advisors
Talavage, Purdue University.
Subject Area
Computer Engineering|Biomedical engineering|Electrical engineering
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