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Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks.
Gotoh, M; Nakaura, T; Funama, Y; Morita, K; Sakabe, D; Uetani, H; Nagayama, Y; Kidoh, M; Hatemura, M; Masuda, T; Hirai, T.
Afiliação
  • Gotoh M; Department of Radiology, Kumamoto University Hospital, Japan.
  • Nakaura T; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan. Electronic address: kff00712@nifty.com.
  • Funama Y; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan.
  • Morita K; Department of Radiology, Kumamoto University Hospital, Japan.
  • Sakabe D; Department of Radiology, Kumamoto University Hospital, Japan.
  • Uetani H; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan.
  • Nagayama Y; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan.
  • Kidoh M; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan.
  • Hatemura M; Department of Radiology, Kumamoto University Hospital, Japan.
  • Masuda T; Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare, Japan.
  • Hirai T; Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Japan.
Radiography (Lond) ; 28(2): 447-453, 2022 05.
Article em En | MEDLINE | ID: mdl-34774411
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Tomografia Computadorizada por Raios X Tipo de estudo: Qualitative_research Limite: Adult / Humans Idioma: En Revista: Radiography (Lond) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Tomografia Computadorizada por Raios X Tipo de estudo: Qualitative_research Limite: Adult / Humans Idioma: En Revista: Radiography (Lond) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão