Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.

Hao WangXiao LiuLingke KongYing HuangHua ChenXiurui MaYanhua DuanYan ShaoAihui FengZhenjiong ShenHengle GuQing KongZhiyong XuYongkang Zhou

Published in: Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] (2023)

The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.

Keyphrases

image quality
deep learning
convolutional neural network
computed tomography
early stage
dual energy
locally advanced
artificial intelligence
radiation therapy
radiation induced
machine learning
spinal cord
optical coherence tomography
squamous cell carcinoma
rectal cancer
human health