基于先验知识和遗传算法的直线加速器光子能谱重建(英文)

为了准确地获得直线加速器的光子能谱,根据测量的百分深度剂量和蒙特卡洛模拟的单能光子百分深度剂量,采用先验约束模型和遗传算法来进行优化求解.首先,将光子能谱建模为一个包含2个参数α和Ep的先验解析函数,采用遗传算法对该模型进行优化求解;然后,将光子能谱建模为一个离散约束优化模型,并利用遗传算法进行优化求解,初始解由第1步获得的解析函数产生.将该方法应用于瓦里安iX直线加速器来计算其6和15 MV光子束的能谱,实验结果表明,采用该方法重建获得的光子能谱以及百分深度剂量与蒙特卡洛模拟计算的结果具有良好的一致性.

Reconstruction of the linac photon spectrum based on prior knowledge and the genetic algorithm

In order to derive the linac photon spectrum accurately both the prior constrained model and the genetic algorithm GA are employed using the measured percentage depth dose PDD data and the Monte Carlo simulated monoenergetic PDDs where two steps are involved.First the spectrum is modeled as a prior analytical function with two parameters αand Ep optimized with the GA.Secondly the linac photon spectrum is modeled as a discretization constrained model optimized with the GA. The solved analytical function in the first step is used to generate initial solutions for the GA’s first run in this step.The method is applied to the Varian iX linear accelerator to derive the energy spectra of its 6 and 15 MV photon beams.The experimental results show that both the reconstructed spectrums and the derived PDDs with the proposed method are in good agreement with those calculated using the Monte Carlo simulation.