使用神经网络计算α粒子单事件光谱
Layth Alkhani1, Jason P Luce2, Pablo Mínguez Gabiña3
1Department of Bioengineering, Stanford University, Stanford, CA, United States.
Frontiers in oncology
|October 17, 2024
概括
一个神经网络准确地预测了微剂量测量的α粒子特定能量光谱. 这个工具有助于理解各种细胞几何形状和能量的辐射效应.
科学领域:
- 医学物理 医学物理
- 计算生物学 计算生物学
- 辐射瘤学 辐射瘤学
背景情况:
- 阿尔法粒子微剂量测量对于辐射风险评估至关重要.
- 预测特定的能量光谱是计算密集的.
- 对于先进的剂量计计算,需要准确的光谱数据.
研究的目的:
- 开发一个神经网络,用于预测α粒子单一事件的特定能量光谱.
- 为了提高微剂量测量计算的效率.
- 为分析生物点中的辐射相互作用提供一种工具.
主要方法:
- 使用蒙特卡洛模拟数据训练了一个具有4个输入和21个输出的神经网络.
- 输入参数包括源-目标配置,α粒子能量,核和细胞半径.
- 网络使用了两个隐藏层和平均平方误差损失函数.
主要成果:
- 神经网络实现了高精度,根平均平方误差 (RMSE) 的zmax低于1.57x10−2.2.
- 对光谱输出的RMSE值在训练,验证和测试数据集中始终很低.
- 相对应系数 (R2) 大于0.98表明预测和实际光谱值之间存在很强的一致性.
结论:
- 训练有素的神经网络准确地复制了α粒子单事件光谱.
- 这种模型对广泛的源-目标几何形状有效.
- 开发的工具可以显著提高微剂量测量计算的速度和准确性.
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