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在碳离子放射治疗中,基于深度学习的蒙特卡洛剂量计算的快速消除

Xinyang Zhang^1,2,3,4, Hui Zhang^1,2,3,5, Jian Wang^1,2,3,4

¹Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.

Medical physics

|September 1, 2023

概括

深度学习的否定加速了碳离子放射治疗计划的验证. GhostUNet显著减少了模拟时间,同时保持了蒙特卡洛剂量分配的高精度.

科学领域:

医学物理医学物理
放射疗法是一种放射治疗.
计算生物学计算生物学

背景情况:

计划验证对于碳离子放射治疗的质量保证至关重要.
传统的幻影测量是耗时和劳动密集的.
蒙特卡洛 (MC) 模拟提供精度,但需要大量的计算时间.

研究的目的:

评估基于深度学习的蒙特卡罗 (MC) denoising,以加快碳离子放射治疗计划验证.
评估在保持剂量分配精度的同时缩短模拟时间的可行性.

主要方法:

三个深度学习模型 (CycleGAN,3DUNet,GhostUNet) 经过训练,将低粒子MC剂量分布 (1x10^6离子) 变为高粒子精度 (1x10^8离子).
在患者数据上训练和测试模型,包括头癌和肺癌.
量化评估使用了根平均平方误差 (RMSE) 和3D马分析 (3mm,3%).

主要成果:

这三种模型都成功地否定了MC的剂量分布,证明了泛化能力.
GhostUNet实现了最低的RMSE (0.075) 和最高的平均马通过率 (99.1%).
GhostUNet需要最小的参数 (4,270万) 和最短的训练时间 (99秒/时代),超过CycleGAN和3DUNet.

结论:

GhostUNet深度学习模型有效地消除了碳离子放射治疗的MC剂量分布.
这种方法显著缩短了计算时间,在不到5秒的时间内提供了与高粒子MC模拟可比的结果.
基于深度学习的Denoising提供了一种有前途的方法来加速放射治疗计划验证.

关键词:

蒙特卡洛模拟的蒙特卡洛模拟碳离子辐射疗法碳离子辐射疗法深度学习是一种深度学习.玛传递率的通过率计划验证计划验证计划

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