9 December 1997 Unified approach to regularized maximum-likelihood estimation in computed tomography
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Abstract
Since 1982, when it was first proposed by Shepp and Vardi, the Expectation Maximization (EM) algorithm has become very popular among researchers in image reconstruction. Recently, a natural extension of the EM algorithm was proposed in order to handle regularization terms containing `a priori' information for emission computed tomography problems. This new idea was further applied to other regularized maximum likelihood problems in transmission and emission tomography. We present in this article a unified approach to more general regularized ML problems. Our convergence proofs also extend those given in the previous papers allowing more general regularizations. We report on numerical simulations.
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Alvaro R. De Pierro, "Unified approach to regularized maximum-likelihood estimation in computed tomography", Proc. SPIE 3171, Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications, (9 December 1997); doi: 10.1117/12.279727; https://doi.org/10.1117/12.279727
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