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|Type:||Artigo de periódico|
|Title:||Convergence results for scaled gradient algorithms in positron emission tomography|
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.|
|Editor:||Iop Publishing Ltd|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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