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基因算法作为检测设置优化的工具,用于质子治疗中的提示性马成像:SiFi Compton相机案例研究

Jonas Kasper¹, Awal Awal^1,2, Ronja Hetzel¹

¹III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany.

Physics in medicine and biology

|July 4, 2025

概括

使用遗传算法优化基于光倍增器和闪纤维的康普顿摄像机 (SiFi-CC) 几何,改善了质子束范围监测. 这种增强的SiFi-CC可以检测质子治疗中的范围转移,以准确的剂量递送.

科学领域:

医学物理医学物理
核仪器仪表核仪器仪表核仪器仪表
辐射瘤学辐射瘤学

背景情况:

质子疗法需要精确监测质子束范围,以有效的剂量递送.
使用康普顿摄像机的快速马 (PG) 检测提供了一种范围验证方法.
基于光倍增器和闪光纤的康普顿摄像头 (SiFi-CC) 是PG检测的一个潜在工具.

研究的目的:

为了证明优化SiFi-CC几何结构用于剂量分配验证的可行性.
使用遗传算法 (GA) 来优化SiFi-CC的几何参数.
为了增强快速的马检测,实时验证质子疗法.

主要方法:

SiFi-CC的关键几何参数 (距离,模块厚度) 使用GA进行了优化.
一个基于Geant4的软件框架模拟了马相互作用,探测器响应和图像重建.
一个健身功能根据检测效率和图像分辨率评估了配置.

主要成果:

优化了GA的SiFi-CC检测到5毫米的质子束范围转移,分辨率为2毫米,使用5x10^8个质子.
最优的几何结构包括16个散射层,36个吸收层,以及特定的源到探测器距离.
最好的配置实现了5.58(1) x 10^-5.5的成像灵敏度.

结论:

优化GA的SiFi-CC可靠地检测出临床相关的质子束范围的变化.
这种优化方法提高了质子治疗中的实时范围验证准确性.
遗传算法提供了一个系统的方法来优化SiFi-CC的性能.

关键词:

康普顿摄像机康普顿摄像机蒙特卡洛模拟的蒙特卡洛模拟.遗传算法是一种遗传算法.提示性马成像技术质子疗法是一种质子疗法.范围验证范围验证范围验证

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