Feature-targeted deep learning framework for pulmonary tumorous Cone-beam CT (CBCT) enhancement with multi-task customized perceptual loss and feature-guided CycleGAN

作者：Jiarui Zhu, Hongfei Sun, Weixing Chen, Shaohua Zhi, Chenyang Liu, Mayang Zhao, Yuanpeng Zhang, Ta Zhou, Y L Lam, Tao Peng, Jing Qin, Lina Zhao, Jing Cai, Ge Ren · 发表于：Computerized Medical Imaging and Graphics · 年份：2025 · DOI：10.1016/j.compmedimag.2024.102487 · 被引用次数：5 · 研究领域：Medical Imaging Techniques and Applications、Advanced Radiotherapy Techniques、Advanced X-ray and CT Imaging

Thoracic Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for lung cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in loss of lung anatomy which contains crucial pulmonary tumorous and functional information. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details containing crucial tumorous information due to lack of targeted guidance. To address this issue, we propose a novel feature-targeted deep learning framework which generates ultra-quality pulmonary imaging from CBCT of lung cancer patients via a multi-task customized feature-to-feature perceptual loss function and a feature-guided CycleGAN. The framework comprises two main components: a multi-task learning feature-selection network (MTFS-Net) for building up a customized feature-to-feature perceptual loss function (CFP-loss); and a feature-guided CycleGan network. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9747 and an average PSNR index of 38.5995 globally, and an average Pearman's coefficient of 0.8929 within the tumor region on multi-institutiona...