Unified approach to regularized maximum likelihood estimation in computed tomography

被引：0
|
作者：
De Pierro, AR [1 ]
机构：
[1] Univ Estadual Campinas, Dept Appl Math, BR-13081970 Campinas, SP, Brazil
来源：
COMPUTATIONAL, EXPERIMENTAL, AND NUMERICAL METHODS FOR SOLVING ILL-POSED INVERSE IMAGING PROBLEMS: MEDICAL AND NONMEDICAL APPLICATIONS | 1997年 / 3171卷
关键词：
maximum likelihood; regularization; majorizing functions; computed tomography;
D O I：
10.1117/12.279727
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Since 1982, when it was first proposed by Shepp and Vardi,(1) the Expectation Maximization (EM) algorithm has become very popular among researchers in image reconstruction. Recently, a natural extension of the EM algorithm was proposed(2) in order to handle regularization terms containing 'a priori' information far emission computed tomography (ECT) problems. This new idea was further applied to other regularized maximum likelihood problems(3-5) in transmision 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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页码：218 / 224
页数：7
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