通过多模式导向剂量预测,用于功能性肺避开放射治疗的自动肺剂量绘画.
Tianyu Xiong1, Guangping Zeng2, Zhi Chen3
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y934, 11 Yuk Choi Rd, Hung Hom, Hong Kong, 000000, HONG KONG.
Physics in medicine and biology
|December 29, 2025
概括
功能性肺避开放射治疗 (FLART) 的新型自动规划算法使用多模式成像准确预测辐射剂量. 这种方法通过利用肺功能数据来提高规划效率和质量.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 医疗成像医学成像
背景情况:
- 功能性肺避开放射治疗 (FLART) 的目的是尽量减少对健康肺组织的辐射剂量.
- 准确预测剂量分配对于有效的FLART规划至关重要.
- 当前的自动规划方法可能无法充分利用声元智能肺功能信息.
研究的目的:
- 为FLART开发基于多模式导向剂量预测 (MMDP) 的自动规划算法.
- 为了利用voxel-wise肺功能图像进行增强的剂量预测和计划生成.
- 提高FLART规划的效率,一致性和质量.
主要方法:
- 开发了一个新的MMDP模型,从多模式图像中提取互补的特征.
- 实施了一个实例权重解剖到功能训练策略,以提高预测准确度.
- 利用功能引导的音量量模拟算法来创建MMDP-FLART计划.
- 用SPECT通风 (V) 和输液 (Q) 图像对回顾性和前性患者数据验证了算法.
主要成果:
- 对于DVH指标,MMDP实现了准确的剂量预测,中位误差在±1Gy/±1%以内.
- 该MMDP模型和培训策略显著减少了功能加权平均肺剂量 (fMLD) 的预测误差.
- 与传统放射治疗 (ConvRT) 计划相比,MMDP-FLART计划显示了fMLD的显著减少.
- MMDP-FLART计划显示的fMLD与手动FLART计划相比相当或较低,对有风险的器官的剂量减少.
结论:
- 具有实例权重解剖到功能训练的MMDP模型使FLART的准确剂量预测成为可能.
- 基于MMDP的自动规划算法有效地使用voxel-wise肺功能数据生成FLART计划.
- 这种方法显示了提高FLART规划效率,一致性和整体质量的潜力.
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