CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.

Liu, Jiwei; Yan, Hui; Cheng, Hanlin; Liu, Jianfei; Sun, Pengjian; Wang, Boyi; Mao, Ronghu; Du, Chi; Luo, Shengquan

Liu, Jiwei; Yan, Hui; Cheng, Hanlin; Liu, Jianfei; Sun, Pengjian; Wang, Boyi; Mao, Ronghu; Du, Chi; Luo, Shengquan.

Afiliación

Liu J; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Yan H; Department of Radiation Oncology, National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cheng H; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Liu J; School of Electrical Engineering and Automation, Anhui University, Hefei, China.
Sun P; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Wang B; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
Mao R; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.
Du C; Cancer Center, The Second Peoples Hospital of Neijiang, Neijiang, China.
Luo S; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.

Quant Imaging Med Surg ; 11(12): 4820-4834, 2021 Dec.

Article en En | MEDLINE | ID: mdl-34888192

Palabras clave

Cone-beam CT; disentangled representation; generative adversarial network; image-guided radiation therapy; synthetic CT generation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Quant Imaging Med Surg Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: China

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