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 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.
Original language | English |
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Article number | 055003 |
Journal | Inverse Problems |
Volume | 30 |
Issue number | 5 |
DOIs | |
State | Published - May 2014 |
Keywords
- blockiterative
- expectation-maximization (EM) algorithm
- ordered subsets expectation maximization (OSEM) algorithm, relaxed EM
- positron emission tomography (PET)
- string-averaging
- string-averaging EM algorithm
ASJC Scopus subject areas
- Theoretical Computer Science
- Signal Processing
- Mathematical Physics
- Computer Science Applications
- Applied Mathematics