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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Helou, Elias Salomao [1] ; Censor, Yair [2] ; Chen, Tai-Been [3] ; Chern, I-Liang [4, 5] ; De Pierro, Alvaro Rodolfo [1] ; Jiang, Ming [6] ; Lu, Henry Horng-Shing [7]
Total Authors: 7
[1] State Univ Sao Paulo, Dept Appl Math & Stat, Sao Carlos, SP - Brazil
[2] Univ Haifa, Dept Math, IL-3190501 Haifa - Israel
[3] I Shou Univ, Dept Med Imaging & Radiol Sci, Kaohsiung 82445 - Taiwan
[4] Natl Chiao Tung Univ, Ctr Math Modeling & Sci Comp, Dept Appl Math, Hsinchu 30010 - Taiwan
[5] Natl Taiwan Univ, Dept Math, Taipei 10617 - Taiwan
[6] Peking Univ, Beijing Int Ctr Math Res, Sch Math Sci, LMAM, Beijing 100871 - Peoples R China
[7] Natl Chiao Tung Univ, Inst Stat, Hsinchu 30010 - Taiwan
Total Affiliations: 7
Document type: Journal article
Source: INVERSE PROBLEMS; v. 30, n. 5 MAY 2014.
Web of Science Citations: 6

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. (AU)

FAPESP's process: 13/16508-3 - Fast computation of the generalized Backprojection operator with applications in tomographic image reconstruction
Grantee:Elias Salomão Helou Neto
Support type: Regular Research Grants