使用RegGAN从使用不同线性加速器获得的CBCT图像中生成合成CT图像
Zhenkai Li1,2, Qingxian Zhang1, Haodong Li2
1Chengdu University of Technology, Chengdu, China.
BMC cancer
|September 5, 2023
概括
注册生成对抗网络 (RegGAN) 模型有效地将形束计算断层扫描 (CBCT) 图像转换为头适应性辐射治疗. 这种AI模型在不同的加速器中表现出强烈的概括性,确保高质量的合成CT图像.
科学领域:
- 医疗成像医学成像
- 人工智能在放射治疗中的应用.
- 放射治疗 物理 物理
背景情况:
- 适应性辐射疗法 (ART) 治疗头癌需要高质量的成像来调整治疗.
- 圆束计算断层扫描 (CBCT) 是放射治疗期间使用的常见成像方式,但其图像质量可能在治疗计划中不足最佳.
- 为改进ART工作流程,开发准确的方法将CBCT图像转换为合成CT (sCT) 图像至关重要.
研究的目的:
- 评估注册生成对抗网络 (RegGAN) 模型的可行性和稳定性,用于CBCT图像转换在头部和部自适应性辐射治疗中.
- 评估RegGAN在不同CBCT成像模型和线性加速器中的性能.
主要方法:
- 训练了RegGAN模型使用100个CBCT和CT图像从头部和部瘤患者.
- 在四个不同的线性加速器的40名患者身上测试了该模型.
- 将生成的合成CT (sCT) 图像的质量与使用平均绝对误差 (MAE),峰值信号对噪声比率 (PSNR) 和结构相似度指数 (SSIM) 的计划CT (pCT) 图像进行了比较.
- 通过分析计划目标体积 (PTV) 剂量统计,评估sCT图像对放射治疗计划的影响.
主要成果:
- 生成的sCT图像呈现出良好的质量,在各种CBCT模式中没有观察到显著差异.
- 雷根模型实现了协同效应的最佳转换效应,显著减少了MAE并改善了PSNR和SSIM.
- 不同的加速器之间没有发现sCT合成的显著差异,这表明强烈的泛化.
结论:
- 雷根模型产生高质量的sCT图像,适合用于头部和部自适应性放射治疗.
- RegGAN模型在不同加速器中展示了强大的泛化能力,使其成为ART的可靠工具.
- 生成的sCT图像可以作为适应性辐射治疗规划和交付的有效参考图像.
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