一个全面的对比研究生成对抗性网络架构的合成计算机断层扫描生成在腹部
Mariia Lapaeva1,2,3, Agustina La Greca Saint-Esteven1,3, Philipp Wallimann1
1Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
从MRI生成合成CT (sCT) 来进行放射治疗计划的最佳方法是使用Pix2Pix生成对抗网络 (GAN) 模型. 这种方法可以提高腹部治疗的图像质量和剂量测量精度.
科学领域:
- 医疗成像医学成像
- 放射治疗规划 放射治疗规划
- 人工智能在医学中的应用
背景情况:
- 基于磁共振 (MR) 的合成计算机断层扫描 (sCT) 生成对于过渡到仅MR的放射治疗计划至关重要.
- 这项技术旨在取代传统的CT扫描,提高成本效益和患者舒适度.
- 生成对抗网络 (GAN) 在sCT生成方面表现出很高的表现.
研究的目的:
- 确定改善基于MR的sCT生成质量和临床适用性的最佳方法.
- 广泛比较腹部sCT生成的GAN架构和参数.
- 为解决缺少用于腹部sCT生成的认证医疗产品的问题.
主要方法:
- 从使用各种GAN (Pix2Pix,CycleGAN,CUT) 和生成器架构 (U-Net,ResNet) 的154名癌症患者的腹部MRI图像生成sCT.
- 包括输入-输出通道 (2D,2.5D) 和训练集大小在内的各种参数.
- 使用图像相似性和剂量测量指标 (DVH参数) 评估sCT质量,评估它们的相关性.
主要成果:
- 带有ResNet生成器和2.5D频道的Pix2Pix模型获得了最佳性能 (MAE 63.21 HU,DVH D平均差异-0.09%).
- 这种配置改善了骨和刚性器官边界的产生,稳健地处理空气口袋.
- 2D通道受益于未配对的GAN (CycleGAN,CUT),提供了临床可用的结果,平均DVH差异低于0.8%.
结论:
- 建立了腹部sCT生成的新基准,证明了放射治疗规划的临床适用性.
- 证实,单靠图像相似度指标无法可靠地预测临床值内的小剂量偏差.
- 确定了与DVH差异高于±5%相关的特定指标,有助于培训,评估和标准化.
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