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We study the maximum likelihood model in emission tomography and propose anew family of algorithms for its solution, called String-AveragingExpectation-Maximization (SAEM). In the String-Averaging algorithmic regime,the index set of all underlying equations is split into subsets, called"strings," and the algorithm separately proceeds along each string, possibly inparallel. Then, the end-points of all strings are averaged to form the nextiterate. SAEM algorithms with several strings presents better practical meritsthan the classical Row-Action Maximum-Likelihood Algorithm (RAMLA). We presentnumerical experiments showing the effectiveness of the algorithmic scheme inrealistic situations. Performance is evaluated from the computational cost andreconstruction quality viewpoints. A complete convergence theory is alsoprovided.

String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography