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Proceedings Paper

Comparison between ML-EM and modified Newton algorithms for SPECT image reconstruction
Author(s): Rita Noumeir; Guy E. Mailloux; Hail Mallouche; Raymond Lemieux
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Paper Abstract

The expectation maximization method for maximum likelihood image reconstruction (ML- EM) is one of the most popular algorithms used in SPECT and PET, because it is based on the realistic assumption that photon emission and counts follow a Poisson process. Moreover, this method retains two important theoretical and practical properties namely nonnegativity and self-normalization of the reconstructed image. This latter property means that the number of emitted photons is equal to the number of counts. However, the major disadvantage of this method is the large amount of computation that is required, due to its slow rate of convergence. In this paper, we demonstrate that the ML-EM algorithm is a special case of the modified Newton method and can thus be accelerated by multiplying at each iteration the changes to the image, as calculated by the standard algorithm, by an overrelaxation parameter. This accelerated ML-EM algorithm can further be optimally accelerated, and converges to a good maximum likelihood estimator.

Paper Details

Date Published: 27 August 1993
PDF: 8 pages
Proc. SPIE 1887, Physiological Imaging, Spectroscopy, and Early-Detection Diagnostic Methods, (27 August 1993); doi: 10.1117/12.151187
Show Author Affiliations
Rita Noumeir, Ecole Polytechnique de Montreal (Canada)
Guy E. Mailloux, Ecole Polytechnique de Montreal (Canada)
Hail Mallouche, Ecole Polytechnique de Montreal (Canada)
Raymond Lemieux, Hopital du Sacre-Coeur de Montreal (Canada)

Published in SPIE Proceedings Vol. 1887:
Physiological Imaging, Spectroscopy, and Early-Detection Diagnostic Methods
Randall Locke Barbour; Mark J. Carvlin, Editor(s)

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