Type
Text
Type
Dissertation
Advisor
Pan, Yingtian | Liang, Jerome | Button, Terry | Gindi, Gene | Moore, William.
Date
2016-12-01
Keywords
computed tomography, low-dose | Biomedical engineering
Department
Department of Biomedical Engineering
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/76980
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Low-dose X-ray computed tomography (CT) imaging is desirable due to the growing concerns about excessive radiation exposure to the patients. However, the reconstructed CT images by the conventional filtered back-projection (FBP) method from the low-dose acquisitions may be severely degraded. Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose CT as compared to the FBP method. According to the maximum a posteriori (MAP) estimation, the SIR methods can be typically formulated by an objective function consisting of two terms: (1) data-fidelity term modeling the statistics of projection measurements, and (2) regularization term reflecting prior knowledge or expectation on the characteristics of the image to be reconstructed. Statistical modeling of the projection measurements is a prerequisite for SIR, while the regularization term in the objective function also plays a critical role for successful image reconstruction. The objective of this dissertation is investigating accurate statistical models and novel regularization strategies for SIR to improve CT image quality in low-dose cases. Specifically, we proposed two texture-preserving regularizations based on the Markov random field (MRF) model and one generic regularization based on the nonlocal means (NLM) filter. The feasibility and efficacy of the proposed strategies are explicitly explored in this dissertation, using both computer simulation and real data (i.e. | physical phantoms and clinical patients). | 92 pages
Recommended Citation
Zhang, Hao, "Statistical image reconstruction for low-dose X-ray computed tomography: statistical models and regularization strategies" (2016). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 2845.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/2845