Data/hora: 05/06/2024 04:44:21

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 19/08/2004
Data da última atualização: | 17/01/2020
Autoria: | DE PIERRO, A. R.; YAMAGISHI, M. E. B.
Afiliação: | Unicamp; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA.
Título: | Fast scaled gradient decomposition methods for maximum likelihood transmission tomography.
Ano de publicação: | 2003
Fonte/Imprenta: | In: ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE EMBS, 25., 2003, Cancun. Proceedings... [S.l.: s.n.], 2003. p. 829-832.
Idioma: | Inglês
Conteúdo: | New iterative algorithms are presented for Maximum Likelihood (ML) and Regularized Maximum Likelihood (MAP) reconstruction in Transmission Tomography (CT). The algorithms are natural extensions to CT of RAMLA, a well known method for ML reconstruction in Emission Computed Tomography (ECT). We show that the new algorithm for ML solutions produces similar, or even better results than EM-like algorithms, but in much fewer iterations. Also, its convergence properties are better than other ordered subsets methods.
Palavras-Chave: | Algoritmo EM; RAMLA; Transmissão de tomografia; Transmission tomography.
Thesagro: | Tomografia.
Thesaurus Nal: | Algorithms; Tomography.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)

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CNPTIA - (10508-1) - UPC - AA - -- -  -  - 2004.00041 - DES
CNPTIA - (10508-2) - UPC - AA - DD -  -  -  - DIS