Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.

Bie, Yifan; Yang, Shuo; Li, Xingchao; Zhao, Kun; Zhang, Changlei; Zhong, Hai

Bie, Yifan; Yang, Shuo; Li, Xingchao; Zhao, Kun; Zhang, Changlei; Zhong, Hai.

Afiliação

Bie Y; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.
Yang S; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.
Li X; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.
Zhao K; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.
Zhang C; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.
Zhong H; Department of Radiology, the Second Hospital, Shandong University, Jinan, China.

Quant Imaging Med Surg ; 13(4): 2197-2207, 2023 Apr 01.

Article em En | MEDLINE | ID: mdl-37064389

Palavras-chave

Deep learning image reconstruction; adaptive statistical iterative reconstruction-Veo (ASiR-V); image quality; kidney; tomography

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Revista: Quant Imaging Med Surg Ano de publicação: 2023 Tipo de documento: Article

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