在脑癌适应性质子疗法中使用计划性MRI增强基于CBCT的CT合成:一种深度学习方法
Juan Manuel Molina-Maza1, David Viar-Hernandez1, Blanca Rodriguez-Gonzalez1
1Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain.
Journal of applied clinical medical physics
|December 11, 2025
概括
磁共振成像 (MRI) 数据改善了用于适应性质子疗法 (APT) 的合成CT (sCT) 生成. 这提高了软组织可视化和在头部癌症治疗中剂量计算的准确性.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 质子疗法 (PT) 使用CT数据提供精确的头部癌症 (HC) 治疗.
- 适应性PT (APT) 纠正了解剖学变化,但依赖于质量有限的CBCT.
- CBCT具有人工物和较低的分辨率,影响剂量准确性.
研究的目的:
- 通过将MRI与CBCT集成来改善合成CT (sCT) 产生APT.
- 在PT治疗规划中提高软组织划分和剂量计算精度.
- 评估使用MRI进行基于CBCT的CT合成的深度学习模型.
主要方法:
- 开发了一个Pix2Pix条件生成对抗网络 (cGAN) 来合成CBCT和T1加权MRI的CT扫描.
- 作为cGAN生成器,ResUnet和SwinUnet架构进行了测试.
- 仅使用CBCT的CycleGAN用于比较.
主要成果:
- 整合MRI数据显著提高了sCT质量,特别是对于眼睛和心室等软组织.
- 结合MRI的Pix2Pix-ResUnet模型超过了仅CBCT和其他深度学习方法.
- 基于MR的sCT减少了HU错误,提高了剂量计算精度.
结论:
- 核磁共振成像集成增强了基于CBCT的CT合成,改善了sCT图像质量和APT中的剂量准确性.
- 这种方法对更精确的头部癌症治疗计划有希望.
- 未来的工作需要更大的数据集和验证MRI在APT中的集成.
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