基于GAN的BNCT放疗剂量预测方法及影响因素

BNCT Dose Prediction Method Based on Generative Adversarial Network and Influencing Factor Analysis

硼剂量是硼中子俘获治疗中实际治疗剂量的重要组成,实现治疗过程中硼剂量实时分布的测量对保证治疗效果至关重要。但目前临床上对硼剂量的监测还缺少切实可行的方法。据此,本文提出利用生成式对抗网络(GAN)根据治疗过程中探测到的478 keV瞬发伽马射线的三维分布预测实时硼剂量三维分布。本研究基于具有中国人生理特征的辐射仿真人体模型,构建了19个脑胶质瘤病例,利用蒙特卡罗方法模拟了头顶照射方案下瞬发伽马射线及硼剂量三维分布,其中15个病例样本作为训练集,4例作为测试集。结果表明,对于复杂肿瘤结构及复杂硼分布的病例,GAN预测结果与蒙特卡罗模拟结果的SSIM系数均约为0.98,表明本方法可实现硼剂量三维分布的准确预测。

Boron dose is an important component of the actual therapeutic dose of boron neutron capture therapy(BNCT).In order to accurately evaluate the actual therapeutic effect of BNCT,it is important to know the real-time distribution of boron dose during the treatment.However,there is still a lack of practical methods for monitoring boron dose in clinical.Accordingly,this work proposed for the first time using generative adversarial network(GAN)to predict the real-time three-dimensional distribution of boron dose based on the three-dimensional distribution detected during treatment.Based on an anthropomorphic male phantom with Chinese phy-siological features,19 cases of glioma were constructed for analysis basing Monte Carlo simulations.The results show that this boron dose prediction method based on GAN can achieve quantitative prediction of the three-dimensional distribution.The value of SSIM between the results generated by Monte Carlo simulation and GAN are all about 0.98 within complex tumor structure and non-uniform distribution of boron concentration in the target area.