Positively constrained multiplicative iterative algorithm for maximum penalized likelihood tomographic reconstruction

Jun Ma*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    18 Citations (Scopus)

    Abstract

    This paper first develops a general multiplicative iterative (MI) algorithm for tomographic image reconstructions, where the objective function is only specified as a general function containing two components: a data mismatch component and a penalty component. This general algorithm is then applied to different objective functions deduced from different probability models for measurements in emission or transmission tomography, such as Poisson, Gaussian, or shifted Poisson models. Furthermore, an approximate line search step can be easily incorporated into the algorithm so that the objective function is guaranteed to increase during the iterations. This MI algorithm (with line search) is easy to implement, as it performs only one forward- and one or two back-projection in each iteration, and it respects the positivity constraint usually imposed on reconstructions.

    Original languageEnglish
    Article number5410007
    Pages (from-to)181-192
    Number of pages12
    JournalIEEE Transactions on Nuclear Science
    Volume57
    Issue number1 PART 1
    DOIs
    Publication statusPublished - Feb 2010

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