Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/57148
Type: Artigo de periódico
Title: Convergence results for scaled gradient algorithms in positron emission tomography
Author: Neto, ESH
De Pierro, AR
Abstract: Positron emission tomography is a well-known technique aiming at reconstructing the emission density of a compound tagged by an artificial isotope inside the body in order to study a given physiological process. The maximum likelihood (ML) approach to PET has been proven to be adequate for modelling this problem and iterative methods provide the option to compute the solutions. In the 1980s the EM (expectation maximization) algorithm was the accepted choice, which was substituted by the faster OS-EM (ordered subsets EM) and its convergent version, RAMLA (row action ML algorithm). In this paper, we present an improved and extended convergence proof for RAMLA, which includes another recently proposed algorithm.
Country: Inglaterra
Editor: Iop Publishing Ltd
Rights: fechado
Identifier DOI: 10.1088/0266-5611/21/6/007
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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