On The Convergence Of The Maximum Likelihood Estimator Method Of Tomographic Image Reconstruction

Jorge Llacer; Eugene Veklerov; Edward J. Hoffman

doi:10.1117/12.966982

1 January 1987 On The Convergence Of The Maximum Likelihood Estimator Method Of Tomographic Image Reconstruction

Jorge Llacer, Eugene Veklerov, Edward J. Hoffman

Proceedings Volume 0767, Medical Imaging; (1987) https://doi.org/10.1117/12.966982
Event: Medical Imaging, 1987, Newport Beach, CA, United States

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.

Citation Download Citation

Jorge Llacer, Eugene Veklerov, and Edward J. Hoffman "On The Convergence Of The Maximum Likelihood Estimator Method Of Tomographic Image Reconstruction", Proc. SPIE 0767, Medical Imaging, (1 January 1987); https://doi.org/10.1117/12.966982

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