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Proceedings Paper

On The Convergence Of The Maximum Likelihood Estimator Method Of Tomographic Image Reconstruction
Author(s): Jorge Llacer; Eugene Veklerov; Edward J. Hoffman
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Paper Abstract

The Maximum Likelihood Estimator (MLE) method of image reconstruction has been reported to exhibit image deterioration in regions of expected uniform activity as the number of iterations increases beyond a certain point. This apparent instability raises questions as to the usefulness of a method that yields images at different stages of the reconstruction that could have different medical interpretations. In this paper we look in some detail into the question of convergence of MLE solutions at a large number of iterations and show that the MLE method converges towards the image that it was designed to yield, i.e. the image which has the maximum likelihood to have generated the specific projection data resulting from a measurement. We also show that the maximum likelihood image can be a very deteriorated version of the true source image and that only as the number of counts in the projection data becomes very high, will the maximum likelihood image converge towards an acceptable reconstruction.

Paper Details

Date Published: 1 January 1987
PDF: 7 pages
Proc. SPIE 0767, Medical Imaging, (1 January 1987); doi: 10.1117/12.966982
Show Author Affiliations
Jorge Llacer, University of California (United States)
Eugene Veklerov, University of California (United States)
Edward J. Hoffman, University of California (United States)


Published in SPIE Proceedings Vol. 0767:
Medical Imaging
Samuel J. Dwyer; Roger H. Schneider, Editor(s)

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