A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography

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Abstract

The maximum likelihood (ML) expectation maximization (EM) approach in emission tomography has been very popular in medical imaging for several years. In spite of this, no satisfactory convergent modifications have been proposed for the regularized approach. Here, a modification of the EM algorithm is presented. The new method is a natural extension of the EM for maximizing likelihood with concave priors. Convergence proofs are given.

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A.R. De Pierro (Wed,) studied this question.

synapsesocial.com/papers/6a1d3af043708a372d5dee35 — DOI: https://doi.org/10.1109/42.370409

Authors

A.R. De Pierro

Intel (United States)

Journals

IEEE Transactions on Medical Imaging

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Universidade Estadual de Campinas (UNICAMP)

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Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

On methods for maximum a posteriori image reconstruction with a normal prior· 1992 · 40 citations
Positron-Emission Tomography· 1980 · 172 citations
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm· 1977 · 49,611 citations
Solution of equations in Euclidean and Banach spaces· 1973 · 678 citations
Image reconstruction from projections : the fundamentals of computerized tomography· 1980 · 2,017 citations

Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

On methods for maximum a posteriori image reconstruction with a normal prior· 1992 · 40 citations
Positron-Emission Tomography· 1980 · 172 citations
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm· 1977 · 49,611 citations
Solution of equations in Euclidean and Banach spaces· 1973 · 678 citations
Image reconstruction from projections : the fundamentals of computerized tomography· 1980 · 2,017 citations

A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography

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Abstract

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Cite this study

Authors

Journals

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Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Also consider

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