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COMPRESSIVE SENSING BASED IMAGE RECONSTRUCTION FOR COMPUTED TOMOGRAPHY DOSE REDUCTION

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abstract
Excessive radiation exposure is one of the major concerns in the computed tomography (CT) field. Few-view reconstruction using iterative algorithm is an important strategy to reduce the radiation dose. In the iterative CT reconstruction, the projection / backprojection model plays an important role in the overall computational cost, image quality, and reconstruction accuracy. In this dissertation, we first propose an improved distance-driven model (IDDM) whose computational cost is as low as the well-known distance-driven model (DDM) and the accuracy is comparable to the accurate area integral model (AIM). Recently, the Lp (0
subject
Compress Sensing
Computed Tomography
Medical Imaging
Non-convex optimization
Reconstruction
contributor
Miao, Chuang (author)
Yu, Hengyong (committee chair)
Wang, Ge (committee member)
Cao, Guohua (committee member)
Plemmons, Robert J. (committee member)
Li, King C. (committee member)
date
2015-08-25T08:35:26Z (accessioned)
2015-08-25T08:35:26Z (available)
2015 (issued)
degree
Biomedical Engineering (discipline)
identifier
http://hdl.handle.net/10339/57252 (uri)
language
en (iso)
publisher
Wake Forest University
title
COMPRESSIVE SENSING BASED IMAGE RECONSTRUCTION FOR COMPUTED TOMOGRAPHY DOSE REDUCTION
type
Dissertation

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