Algorithms 2013, 6(1), 136-160; doi:10.3390/a6010136
Review

Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging

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Received: 28 November 2012; in revised form: 18 February 2013 / Accepted: 19 February 2013 / Published: 12 March 2013
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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Abstract: Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively constrained simultaneous iterative algorithms for maximum penalized likelihood reconstructions, where all measurements are used to estimate all pixel intensities in each iteration.
Keywords: tomographic imaging; penalized likelihood; algorithms; constrained optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Ma, J. Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging. Algorithms 2013, 6, 136-160.

AMA Style

Ma J. Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging. Algorithms. 2013; 6(1):136-160.

Chicago/Turabian Style

Ma, Jun. 2013. "Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging." Algorithms 6, no. 1: 136-160.

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