Iterative reconstruction methods of CT images using a statistical framework Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/m613n2759

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  • Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography (CT) is currently the most commonly used medical imaging technology because it is easy to use, detectors and scanners are constantly improving, and more importantly, patients receive less radiation compared to other imaging technologies. This thesis focuses on improving CT reconstruction algorithms by incorporating prior knowledge of the tissues being scanned. A Gaussian Mixture Prior, and Gibbs sampling is introduced into the reconstruction framework and solved using Maximum-a-posterior (MAP). As a comparison, the images were also reconstructed using unregularized and regularized Maximum Likelihood (ML).
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