一个开源的深度学习框架,用于在放射治疗期间进行呼吸运动监测和体积成像
Nicholas Hindley1, Paul J Keall1
1Image X Institute, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
Medical physics
|July 15, 2025
概括
Voxelmap是一个深度学习框架,可以使用标准临床资源实时监测呼吸运动和体积成像. 这种方法可以同时跟踪目标和处于危险的器官,提高辐射治疗的准确性.
科学领域:
- 医学物理 医学物理
- 医疗保健中的人工智能
- 辐射瘤学 辐射瘤学
背景情况:
- 实时图像引导辐射疗法 (IGRT) 由于专门设备的要求,其采用程度有限.
- 现有的IGRT技术往往只关注目标,忽视周围的危险器官 (OAR).
研究的目的:
- 开发Voxelmap,这是一个廉价的深度学习框架,用于实时3D呼吸运动估计和体积成像.
- 为了适应Voxelmap标准的临床环境和各种成像模式,如MRI-Linacs.
- 为了实现拓保护和可逆映射,以增强运动跟踪.
主要方法:
- 使用4D-CT或4D-MRI扫描从可变形图像登记生成的特定患者培训数据集.
- 采用五种深度学习网络架构,从二维成像数据 (x射线,MR或k空间) 预测3D变形向量场 (DVF).
- 使用Voxelmap来扭曲图像和轮,同时进行目标和OAR跟踪和体积成像.
主要成果:
- 在数字幻影上实现了对目标和OAR轮的高Dice相似性 (MRI高达0.91,X射线高达0.89).
- 与标准方法相比,已证明具有竞争力的目标心脏误差 (在X射线网络A中低至1.8毫米).
- 与标准护理方法相比,动态转移的目标量减少了31%.
结论:
- Voxelmap 提供了一个通用的开源解决方案,用于内断层呼吸运动监测和体积成像.
- 确定了在临床环境中更强大的特定网络架构,为未来的实时运动跟踪发展提供信息.
- 该框架的代码是公开可用的,用于进一步的研究和应用.
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