String-averaging expectation-maximization for maximum likelihood estimation in emission tomography

Elias Salomão Helou, Yair Censor, Tai Been Chen, I. Liang Chern, Álvaro Rodolfo De Pierro, Ming Jiang, Henry Horng Shing Lu

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


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 present better practical merits than the classical row-action maximum-likelihood algorithm. We present numerical experiments showing the effectiveness of the algorithmic scheme, using data of image reconstruction problems. Performance is evaluated from the computational cost and reconstruction quality viewpoints. A complete convergence theory is also provided.

期刊Inverse Problems
出版狀態Published - 1 1月 2014


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