@article{QIMS155639,
author = {Yuhan Fan and Peng Huang and Jiawen Shang and Zhixing Chang and Zhihui Hu and Ke Zhang and Xin Xie and Zhiqiang Liu and Hui Yan},
title = {Synthesizing cone-beam projections in radiotherapy using deep learning network for patients with head and neck cancer},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {16},
number = {7},
year = {2026},
keywords = {},
abstract = {Background: Initially, cone-beam (CB) projection was developed to serve as a two-dimensional (2D) radiograph for fast target localization in radiotherapy. Later, it was gradually replaced by three-dimensional (3D) cone-beam computed tomography (CBCT), which is reconstructed from multiple CB projections. Since the size of CB projections is relatively large, they are usually discarded to conserve clinical storage. To effectively regenerate these projections, a deep learning (DL) method was developed to synthesize CB projections from CBCT.Methods: CB projection images from 50 patients under image-guided radiotherapy for head and neck cancer was collected. First, the digitally reconstructed radiograph (DRR) was created via a ray-tracing based algorithm. Next, a DL network was built to learn the pixel-to-pixel correspondence between the DRRs and CB projections in the training set. Finally, the CB projections were synthesized from DRRs in the testing set by a DL model. Three DL networks, Attention-UNet, Residual AutoEncoder, and Pix2Pix, were examined. Ablation studies on the effects of scatter correction, CBCT resolution, and training sample size were conducted. Non-DL and DL methods were compared, and a clinically relevant downstream task was conducted. Three similarity metrics, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and video quality metric (VQM), were used to evaluate model performance.Results: Among three DL models, Attention-UNet achieved the best performance in terms of PSNR (29.71), SSIM (0.97), and VQM (0.20). The other two models showed comparable performance with PSNR, SSIM, and VQM values of 26.55, 0.96, and 0.27, respectively, for Residual AutoEncoder, and of 27.22, 0.94, and 0.25, respectively, for Pix2Pix. With scatter correction, the PSNR, SSIM, and VQM for Attention-UNet improved by 21.3%, 2.1%, and 39.3%, respectively. Under the finest voxel size (0.5×0.5×2.0 mm3), Attention-UNet achieved the highest PSNR (29.71±3.62), SSIM (0.97±0.008), and lowest VQM (0.20±0.091). The scatter correction and higher CBCT resolution could effectively improve the prediction accuracy of these DL models.Conclusions: The DL-based method can effectively synthesize CB projections from CBCT for patients with head and neck cancer. It may thus serve as a means to saving storage space for clinical CB projections.},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/155639}
}