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

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

Department of Statistics, Macquarie University, North Ryde, New South Wales 2109, Australia
Received: 28 November 2012 / Revised: 18 February 2013 / Accepted: 19 February 2013 / Published: 12 March 2013
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
View Full-Text   |   Download PDF [297 KB, uploaded 12 March 2013]

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. View Full-Text
Keywords: tomographic imaging; penalized likelihood; algorithms; constrained optimization tomographic imaging; penalized likelihood; algorithms; constrained optimization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

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