Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT

ObjectiveTo develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).MethodsThis study included 100 post-breast-c...

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Bibliographic Details
Main Authors: Na Li, Xuanru Zhou, Shupeng Chen, Jingjing Dai, Tangsheng Wang, Chulong Zhang, Wenfeng He, Yaoqin Xie, Xiaokun Liang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-02-01
Series:Frontiers in Oncology
Subjects:
synthetic image
deformable image registration (DIR)
breast cancer
deep learning
radiation therapy
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1127866/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2023.1127866/full

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