在X射线光束蒙特卡洛模拟中基于机器学习的阳极效应的建模
Hussein Harb1, Didier Benoit1, Axel Rannou1
1LaTIM, University of Brest, INSERM UMR1101, Brest, France.
Physics in medicine and biology
|December 15, 2025
概括
本研究引入了一种机器学习框架,用于准确地模拟X射线成像系统的蒙特卡洛模拟中阳极效应. 这种方法提高了模拟现实性,减少了实验校准.
科学领域:
- 医学物理 医学物理
- 计算成像技术的成像
- 机器学习 机器学习
背景情况:
- 阳极效应导致X射线束强度不对称,影响成像系统模拟.
- 这种效应的准确建模对于医学成像中的现实的蒙特卡洛 (MC) 模拟至关重要.
研究的目的:
- 开发一种机器学习 (ML) 框架,用于在MC模拟中建模阳极效应.
- 以最小的实验数据来实现精确的,能源依赖的光束建模.
主要方法:
- 训练了多重回归模型,包括梯度增强回归 (GBR),以预测空间强度变化.
- 使用来自光束测量和微调协议的实验获得的重量.
- 在OpenGATE和GGEMS MC工具包中实现了GBR模型.
主要成果:
- GBR实现了最高的精度,预测误差低于5%的能量水平.
- 一个优化的微调策略减少了65%的测量工作量 (6个位置/能量水平).
- 基于ML的模型密切地复制了临床光束配置文件,性能优于对称模型.
结论:
- 介绍了一种强大的ML方法,用于将阳极效应纳入MC模拟中.
- 增强对剂量计,图像质量和辐射保护应用的模拟现实性.
- 在有限的校准数据下提供精确的光束建模.
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