Chengdu University of Technology, Chengdu, China.
Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
BMC Cancer. 2023 Sep 5;23(1):828. doi: 10.1186/s12885-023-11274-7.
The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models.
A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model's accuracy.
The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators.
The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.
本研究旨在探讨注册生成对抗网络（RegGAN）模型在头颈部自适应放疗图像转换中的可行性及其在不同锥形束 CT（CBCT）模型下的稳定性。
共纳入 100 例头颈部肿瘤患者的 CBCT 和 CT 图像进行训练阶段，40 例不同患者的图像则来自 4 种不同的直线加速器，用于测试阶段。通过训练和测试评估 RegGAN 模型的性能。采用平均绝对误差（MAE）、峰值信噪比（PSNR）和结构相似性指数度量（SSIM）等指标比较生成的合成 CT（sCT）图像质量与计划 CT（pCT）图像质量。此外，还将放射治疗计划均匀应用于 sCT 和 pCT 图像，分析计划靶区（PTV）剂量统计数据，并计算剂量差异率，以验证模型的准确性。
生成的 sCT 图像质量良好，不同 CBCT 模式之间无显著差异。RegGAN 对 Synergy 的转换效果最佳，MAE 从 231.3±55.48 降至 45.63±10.78；PSNR 从 19.40±1.46 增至 26.75±1.32；SSIM 从 0.82±0.02 增至 0.85±0.04。RegGAN 生成的 sCT 图像质量改善效果明显，不同加速器之间无明显 sCT 合成差异。
RegGAN 模型生成的 sCT 图像质量较高，RegGAN 模型在不同加速器之间具有较强的泛化能力，可将其输出作为头颈部自适应放疗的参考图像。
