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Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies.
Sakai, Yuki; Hida, Tomoyuki; Matsuura, Yuko; Kamitani, Takeshi; Onizuka, Yasuhiro; Shirasaka, Takashi; Kato, Toyoyuki; Ishigami, Kousei.
Afiliación
  • Sakai Y; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Hida T; Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Matsuura Y; Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Kamitani T; Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Onizuka Y; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Shirasaka T; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Kato T; Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
  • Ishigami K; Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan.
Br J Radiol ; 96(1141): 20220731, 2023 Jan 01.
Article en En | MEDLINE | ID: mdl-36318483
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Interpretación de Imagen Radiográfica Asistida por Computador / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Br J Radiol Año: 2023 Tipo del documento: Article País de afiliación: Japón
Texto completo
Imprimir
XML
PubMed Links
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Interpretación de Imagen Radiográfica Asistida por Computador / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Br J Radiol Año: 2023 Tipo del documento: Article País de afiliación: Japón