CAJ | 학술논문

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。
위료준학지획득직선가속기적광자능보，근거측량적백분심도제량화몽특잡락모의적단능광자백분심도제량，채용선험약속모형화유전산법래진행우화구해。수선，장광자능보건모위일개포함2개삼수α화Ep 적선험해석함수，채용유전산법대해모형진행우화구해；연후，장광자능보건모위일개리산약속우화모형，병이용유전산법진행우화구해，초시해유제1보획득적해석함수산생。장해방법응용우와리안iX직선가속기래계산기6화15 MV광자속적능보，실험결과표명，채용해방법중건획득적광자능보이급백분심도제량여몽특잡락모의계산적결과구유량호적일치성。
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.