On iterative Bayes algorithms for emission tomography

Ma Jun*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)
    6 Downloads (Pure)


    In this paper we formulate a new approach to medical image reconstruction from projections in emission tomography. This approach conceptually differs from the traditional methods such as filtered backprojection, maximum likelihood or maximum penalized likelihood. Similar to the Richardson-Lucy algorithm ([1], [2]), our method develops directly from the Bayes formula with the final result being an iterative algorithm, for which the maximum likelihood expectation-maximization of [3] (or [4]) is a special case. Although there are different ways to enforce smoothness in the reconstructions using this method, in this paper we opt to focus only on the way which smoothes the camera bin measurements before reconstruction. In fact, this method can be explicated as maximizing a special penalized log-likelihood function. Its theoretical properties are also analyzed in the paper.

    Original languageEnglish
    Article number4545158
    Pages (from-to)953-966
    Number of pages14
    JournalIEEE Transactions on Nuclear Science
    Issue number3
    Publication statusPublished - Jun 2008

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    • EM
    • Iterative Bayes algorithm
    • MPL
    • Smoothed sinogram


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