基于生成对抗网络的鼻咽癌患者伪CT合成方法研究

Generative Adversarial Networks based synthetic-CT generation for patients with nasopharyngeal carcinoma

目的 研究利用生成对抗网络(GAN)建立头颈部肿瘤MRI图像与CT图像的映射模型,实现MRI引导放疗中伪CT (sCT)的预测生成。方法 收集45例鼻咽癌患者治疗前影像信息与IMRT计划信息。首先对MRI (T1)和CT图像进行刚性配准、裁剪、去背景、数据增强等预处理操作;其次对病例进行GAN训练,随机选取30例作为训练集放入网络进行建模学习,另15例用于测试。比较预测sCT与真实CT的图像质量,以及后续比较预测sCT进行重计算的剂量分布与真实计划的剂量分布。结果 测试集的预测sCT与实际CT图像质量比较显示,二者误差较小,体素平均绝对误差值为(79.15±11.37) HU,结构相似性系数值为0.83±0.03。sCT重计算的剂量分布与实际剂量较为接近,不同区域水平下的MAE值相对处方剂量均<1%。在2mm/2%、3mm/3%准则下,所有病例sCT重计算剂量分布的γ通过率均>92%、>98%。结论 提出并实现了使用GAN进行鼻咽癌患者sCT的生成,为MR-IGRT实施奠定了基础。图像质量与剂量学比较均显示了方法的可行性与准确性。

Objective To establish a correlation model between MRI and CT images to generate synthetic-CT(sCT)of head and neck cancer during MRI-guided radiotherapy by using generative adversarial networks(GAN).Methods Images and IMRT plans of 45 patients with nasopharyngeal carcinoma were collected before treatment.Firstly,the MRI(T1)and CT images were preprocessed,including rigid registration,clipping,background removal and data enhancement,etc.Secondly,the cases were trained by GAN,of which 30 cases were randomly selected and put into the network as training set images for modeling and learning,and the other 15 cases were used for testing.The image quality of predicted sCT and real CT were statistically compared,and the dose distribution recalculated upon predicted sCT was statistically compared with that of real planned dose distribution.Results The mean absolute error of the predicted sCT of the testing set was(79.15±11.37)HU,and the SSIM value was 0.83±0.03.The MAE values of dose distribution difference at different regional levels were less than 1%compared to the prescription dose.The gamma passing rate of the sCT dose distribution was higher than 92%and 98%under the 2mm/2%and 3mm/3%criteria.Conclusions We have successfully proposed and realized the generation of sCT for head and neck cancer using GAN,which lays a foundation for the implementation of MRI-guided radiotherapy.The comparison of image quality and dosimetry shows the feasibility and accuracy of this method.