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Deep-learning reconstruction to improve image quality of myocardial dynamic CT perfusion: comparison with hybrid iterative reconstruction.
Takafuji, M; Kitagawa, K; Mizutani, S; Oka, R; Kisou, R; Sakaguchi, S; Ichikawa, K; Izumi, D; Sakuma, H.
Afiliação
  • Takafuji M; Department of Radiology, Mie University Graduate School of Medicine, Tsu, Japan; Department of Radiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Kitagawa K; Department of Radiology, Mie University Graduate School of Medicine, Tsu, Japan. Electronic address: kakuya@med.mie-u.ac.jp.
  • Mizutani S; Department of Radiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Oka R; Department of Radiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Kisou R; Department of Radiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Sakaguchi S; Department of Cardiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Ichikawa K; Department of Cardiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Izumi D; Department of Cardiology, Matsusaka Municipal Hospital, Matsusaka, Japan.
  • Sakuma H; Department of Radiology, Mie University Graduate School of Medicine, Tsu, Japan.
Clin Radiol ; 77(10): e771-e775, 2022 10.
Article em En | MEDLINE | ID: mdl-35853777
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Limite: Humans Idioma: En Revista: Clin Radiol 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: Aprendizado Profundo Limite: Humans Idioma: En Revista: Clin Radiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão