Date of Award

Spring 5-15-2014

Author's School

School of Engineering & Applied Science

Author's Department

Electrical & Systems Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

Two classes of subsampling strategies, partially inspired by ideas from compressed sensing (CS), are developed and tested using real medical x-ray CT data acquired with a helical geometry. A version of the Feldkamp algorithm for helical x-ray CT is described. An alternating minimization (AM) algorithm for finding the maximum-likelihood estimates of attenuation functions in transmission X-ray tomography, developed by O’Sullivan and Benac, is then introduced. The derivation of this AM algorithm is extended to include an optional regularization term, which makes it a MAP estimate. A Newton’s method with trust region modification is implemented for the regularization. In addition, the alternating minimization (AM) algorithm when using data from a subset of detectors, developed by Snyder, is illustrated. Ordered subsets techniques are used to increase the convergence rate. Results of subsampling strategies are demonstrated on real data by subsampling the actual measurements and reconstructing.

Language

English (en)

Chair

Arye Nehorai

Committee Members

Arye Nehorai

Comments

Permanent URL: https://doi.org/10.7936/K7X63JVN

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