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PubMed · 15248569

An accelerated convergent ordered subsets algorithm for emission tomography.

Ing-Tsung Hsiao·Anand Rangarajan·Parmeshwar Khurd·Gene Gindi
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Abstract

We propose an algorithm, E-COSEM (enhanced complete-data ordered subsets expectation-maximization), for fast maximum likelihood (ML) reconstruction in emission tomography. E-COSEM is founded on an incremental EM approach. Unlike the familiar OSEM (ordered subsets EM) algorithm which is not convergent, we show that E-COSEM converges to the ML solution. Alternatives to the OSEM include RAMLA, and for the related maximum a posteriori (MAP) problem, the BSREM and OS-SPS algorithms. These are fast and convergent, but require ajudicious choice of a user-specified relaxation schedule. E-COSEM itself uses a sequence of iteration-dependent parameters (very roughly akin to relaxation parameters) to control a tradeoff between a greedy, fast but non-convergent update and a slower but convergent update. These parameters are computed automatically at each iteration and require no user specification. For the ML case, our simulations show that E-COSEM is nearly as fast as RAMLA.

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AlgorithmsImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalNumerical Analysis, Computer-AssistedPhantoms, ImagingReproducibility of ResultsSensitivity and SpecificityTomography, Emission-Computed, Single-PhotonComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.Validation Study

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Ing-Tsung Hsiao, Anand Rangarajan, Parmeshwar Khurd, Gene Gindi. 2004-06-07. An accelerated convergent ordered subsets algorithm for emission tomography.. https://doi.org/10.1088/0031-9155%2F49%2F11%2F002

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