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Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.
Mikayama, Ryoji; Shirasaka, Takashi; Kojima, Tsukasa; Sakai, Yuki; Yabuuchi, Hidetake; Kondo, Masatoshi; Kato, Toyoyuki.
Afiliación
  • Mikayama R; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Shirasaka T; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Kojima T; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Sakai Y; Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Yabuuchi H; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
  • Kondo M; Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Kato T; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
Br J Radiol ; 95(1130): 20210915, 2022 Feb 01.
Article en En | MEDLINE | ID: mdl-34908478
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Nódulo Pulmonar Solitario / Fantasmas de Imagen / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Br J Radiol Año: 2022 Tipo del documento: Article País de afiliación: Japón
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Nódulo Pulmonar Solitario / Fantasmas de Imagen / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Br J Radiol Año: 2022 Tipo del documento: Article País de afiliación: Japón