Data/hora: 05/06/2024 08:35:42

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