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基于机器学习的时间和光谱快速马射线信息的集成,用于质子范围验证

Aaron Kieslich^1,2, Sonja M Schellhammer^1,2,3, Alex Zwanenburg^1,4,5

¹OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.

Physics and imaging in radiation oncology

|June 30, 2025

概括

快速马射线计时 (PGT) 提供了精确的质子范围验证. 整合光谱数据并没有提高准确性,仅显示时间信息就足以在质子辐射疗法中可靠地监测剂量.

科学领域:

医学物理医学物理
辐射瘤学辐射瘤学
核仪器仪表核仪器仪表核仪器仪表

背景情况:

快速马射线定时 (PGT) 和光谱 (PGS) 是监测质子放射治疗的关键非侵入性方法.
整合PGT和PGS可以通过结合时间和光谱数据来增强质子范围验证.
机器学习正在探索,以提高质子范围验证的准确性.

研究的目的:

评估整合快速马射线定时和光谱数据的有效性,以加强质子范围验证.
评估频谱信息与时间信息相比在机器学习模型中的贡献,用于范围转移预测.
为了确定仅仅是时间信息是否足以进行精确的质子范围验证.

主要方法:

用质子束 (162 和 225 MeV) 辐射一个均的幻影.
引入了模拟的解剖变异 (空气腔).
使用PGT探测器测量了即时马射线,提取了2D时间能量光谱.
机器学习模型使用不同的特征集 (仅能量,仅时间,组合,图像) 进行训练,以预测范围变化,并通过RMSE评估性能.

主要成果:

只有时间和综合时间能量的功能实现了良好的性能 (RMSE 3-4 mm).
仅能量和图像功能表现较差 (RMSE>5毫米).
整合仅使用能量功能的功能并没有提高预测准确度,而不是仅使用时间功能的功能.

结论:

在这个设置中,光谱信息没有为质子束范围转移的确定提供额外的价值.
仅从快速马射线获得的时间信息就足以准确验证质子范围.
这项研究验证了PGT在质子疗法中可靠范围验证的使用.

关键词:

机器学习机器学习多变量建模多变量建模快速的马射线定时时间质子辐射疗法质子辐射疗法处理验证处理验证

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