A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images

A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images

Abstract Background Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of syn...

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Bibliographic Details
Main Authors: Keisuke Usui, Koichi Ogawa, Masami Goto, Yasuaki Sakano, Shinsuke Kyougoku, Hiroyuki Daida
Format: Article
Language:English
Published: BMC 2022-04-01
Series:Radiation Oncology
Subjects:
CycleGAN
4D-CBCT
ART
Image quality correction
Lung cancer
Online Access:https://doi.org/10.1186/s13014-022-02042-1

Internet

https://doi.org/10.1186/s13014-022-02042-1

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