一种节省时间和空间的蒙特卡洛模拟方法,使用后结合生成对抗网络来计算O环门架Linac的剂量
Mengying Shi1, Sunan Cui2, Cynthia Chuang3
1Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of California, Irvine, Orange, CA, USA.
生成对抗网络 (GAN) 准确地模拟了RefleXion X1 Linac,大大减少了放射治疗模拟的计算时间和存储需求.
科学领域:
- 医学物理 医学物理
- 放射治疗技术 放射治疗技术
- 计算机建模 计算建模
背景情况:
- RefleXion X1 Linac是一种先进的放射治疗机器,采用二元多叶合并 (MLC) 系统.
- 该系统可实现创新的生物指导放射治疗技术.
研究的目的:
- 评估使用生成对抗网络 (GANs) 来建模RefleXion X1 Linac. 的可行性.
- 评估基于GAN的剂量模拟的准确性.
- 确定这种方法的潜在计算效益.
主要方法:
- 开发了34个GAN发电机,每个发电机都对特定MLC开口的相位空间文件进行了训练.
- 在水中模拟剂量分布,使用GAN和相位空间源进行比较.
- 通过绕过聚合模拟,估计节省了计算时间和减少了存储.
主要成果:
- GAN模拟显示,对于百分比剂量深度,半阴影和全宽半最大,与相空间模拟具有很高的一致性.
- 马通道率 (1%/1mm) 在所有光圈上都超过了90%.
- 估计节省了530个CPU小时的时间,并减少了102倍的存储使用量,为一个有5766个beamlet的计划.
结论:
- 生成对抗网络 (GAN) 为模拟RefleXion X1 Linac.提供了一个准确而高效的方法.
- 计算时间和存储的显著减少使得GAN对于未来的剂量测量和放射治疗中的光束建模非常有价值.
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