Open AccessCBCT-based synthetic CT generation using generative adversarial networks with disentangled representation
Author(s) -
Jiwei Liu,
Hui Yan,
Hanlin Cheng,
Jianfei Liu,
Pengjian Sun,
Boyi Wang,
Ronghu Mao,
Chi Du,
Luo Sheng-quan
Publication year - 2021
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4306
pISSN - 2223-4292
DOI - 10.21037/qims-20-1056
Subject(s) - mean squared error , computer science , generative adversarial network , artificial intelligence , similarity (geometry) , image quality , pattern recognition (psychology) , representation (politics) , cone beam computed tomography , peak signal to noise ratio , image guided radiation therapy , deep learning , image (mathematics) , computed tomography , mathematics , nuclear medicine , medicine , medical imaging , radiology , statistics , political science , politics , law
Cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT), however its poor image quality limited its clinical application. In this study, we developed a deep-learning based approach to translate CBCT image to synthetic CT (sCT) image that preserves both CT image quality and CBCT anatomical structures.
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