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Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.
Oostveen, Luuk J; Meijer, Frederick J A; de Lange, Frank; Smit, Ewoud J; Pegge, Sjoert A; Steens, Stefan C A; van Amerongen, Martin J; Prokop, Mathias; Sechopoulos, Ioannis.
Afiliação
  • Oostveen LJ; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands. Luuk.Oostveen@radboudumc.nl.
  • Meijer FJA; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • de Lange F; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Smit EJ; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Pegge SA; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Steens SCA; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • van Amerongen MJ; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Prokop M; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Sechopoulos I; Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
Eur Radiol ; 31(8): 5498-5506, 2021 Aug.
Article em En | MEDLINE | ID: mdl-33693996
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2021 Tipo de documento: Article