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• W2020094615 abstract "Statistical iterative reconstruction (SIR) methods have shown remarkable gains over the conventional filteredbackprojection (FBP) method in improving image quality for low-dose computed tomography (CT). They reconstructthe CT images by maximizing/minimizing a cost function in a statistical sense, where the cost function usually consistsof two terms: the data-fidelity term modeling the statistics of measured data, and the regularization term reflecting aprior information. The regularization term in SIR plays a critical role for successful image reconstruction, and anestablished family of regularizations is based on the Markov random field (MRF) model. Inspired by the success of nonlocalmeans (NLM) algorithm in image processing applications, we proposed, in this work, a family of generic and edgepreservingNLM-based regularizations for SIR. We evaluated one of them where the potential function takes thequadratic-form. Experimental results with both digital and physical phantoms clearly demonstrated that SIR with theproposed regularization can achieve more significant gains than SIR with the widely-used Gaussian MRF regularizationand the conventional FBP method, in terms of image noise reduction and resolution preservation." @default.
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• W2020094615 date "2014-03-19" @default.
• W2020094615 modified "2023-09-27" @default.
• W2020094615 title "Nonlocal means-based regularizations for statistical CT reconstruction" @default.
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• W2020094615 doi "https://doi.org/10.1117/12.2043949" @default.
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