Algorithms for non-negatively constrained maximum penalized likelihood reconstruction in tomographic imaging

Jun Ma*

*Corresponding author for this work

    Research output: Contribution to journalArticle

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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.

    Original languageEnglish
    Pages (from-to)136-160
    Number of pages25
    JournalAlgorithms
    Volume6
    Issue number1
    DOIs
    Publication statusPublished - 2013

    Bibliographical note

    Copyright 2013 by the author; licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please check publisher website http://www.mdpi.com/home.

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