%0 Journal Article
%T String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography
%A E. S. Helou
%A Y. Censor
%A T. -B. Chen
%A I-L. Chern
%A ив. R. De Pierro
%A M. Jiang
%A H. H. -S. Lu
%J Physics
%D 2014
%I arXiv
%X We study the maximum likelihood model in emission tomography and propose a new family of algorithms for its solution, called String-Averaging Expectation-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 in parallel. Then, the end-points of all strings are averaged to form the next iterate. SAEM algorithms with several strings presents better practical merits than the classical Row-Action Maximum-Likelihood Algorithm (RAMLA). We present numerical experiments showing the effectiveness of the algorithmic scheme in realistic situations. Performance is evaluated from the computational cost and reconstruction quality viewpoints. A complete convergence theory is also provided.
%U http://arxiv.org/abs/1402.2455v1