Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

Feng, Zhiwei; Cai, Ailong; Wang, Yizhong; Li, Lei; Tong, Li; Yan, Bin

Feng, Zhiwei; Cai, Ailong; Wang, Yizhong; Li, Lei; Tong, Li; Yan, Bin.

Afiliação

Feng Z; Zhong Yuan Network Security Research Institute, Zhengzhou University, Zhengzhou, Henan, China.
Cai A; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Wang Y; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Li L; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Tong L; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Yan B; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.

J Xray Sci Technol ; 29(1): 91-109, 2021.

Article em En | MEDLINE | ID: mdl-33459686

Assuntos

Processamento de Imagem Assistida por Computador; Redes Neurais de Computação; Artefatos; Humanos; Razão Sinal-Ruído; Tomografia Computadorizada por Raios X

Palavras-chave

Low-dose CT; deep learning; image denoising; residual network

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação Idioma: En Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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