String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography
Abstract
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.
Cite
@article{arxiv.1402.2455,
title = {String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography},
author = {E. S. Helou and Y. Censor and T. -B. Chen and I-L. Chern and Á. R. De Pierro and M. Jiang and H. H. -S. Lu},
journal= {arXiv preprint arXiv:1402.2455},
year = {2019}
}