摘要
目的观察循环生成对抗网络(CycleGAN)用于对鼻咽癌兆伏级CT(MVCT)图像进行迁移修正的价值。方法纳入101例于自适应放射治疗前接受计划CT(pCT)及MVCT扫描的鼻咽癌患者,随机将其分为训练集(n=80)和测试集(n=21)。基于CycleGAN对训练集图像进行训练并生成模型,再以模型对测试集MVCT进行迁移修正而生成伪CT(sCT);对pCT进行重采样,使其体素及尺寸与MVCT一致,获得重采样CT(RCT)。勾画肿瘤靶区(GTV)和危及器官并计算剂量,比较MVCT与RCT、sCT与RCT图像的CT值平均绝对误差(MAE)和剂量分布。结果测试集MVCT与RCT图像之间、sCT与RCT图像之间CT值的MAE分别为132.67(121.84,138.28)HU及76.77(62.71,86.43)HU,差异有统计学意义(Z=-5.466,P<0.001)。除颈部右侧转移淋巴结(GTV-NR-P)D 95和左晶状体D_(max)外,sCT与RCT图像其余剂量参数差异均无统计学意义(P均>0.05)。结论以CycleGAN修正鼻咽癌MVCT图像可使其与千伏级pCT图像的CT值差距明显缩小并能用于计算剂量。
Objective To observe the value of cycle generative adversarial network(CycleGAN)for transfer correction of megavoltage CT(MVCT)images of nasopharyngeal carcinoma.Methods Totally 101 patients with nasopharyngeal carcinoma who underwent planned CT(pCT)and MVCT scanning before adaptive radiotherapy were enrolled and divided into training set(n=80)or test set(n=21).Based on CycleGAN,the images in training set were trained to generate a model,MVCT of test set were migrated and modified to generate synthesized CT(sCT)using this model.Then pCT were resampled to make the voxel and size consistent with MVCT to obtain resampled CT(RCT).The gross target volume(GTV)and affected organ structure were drawn,and the dose was calculated.The mean absolute error(MAE)of CT value and dose distribution were compared between MVCT and RCT,as well as between sCT and RCT.Results In test set,MAE of CT value between MVCT and RCT was 132.67(121.84,138.28)HU,between sCT and RCT was 76.77(62.71,86.43)HU,respectively,being significant different(Z=-5.466,P<0.001).Except for D 95 of the metastatic lymph node of the right side of neck(GTV-node right-plan target volume,GTV-NR-P)and D_(max)of the left crystalline lens,no significant difference of other dose parameters was found between sCT and RCT images(all P>0.05).Conclusion Correcting MVCT image of nasopharyngeal carcinoma with CycleGAN could significantly reduce its CT value difference with kilovolt pCT image,hence being able to be used for dose calculation.
作者
黄星武
傅万凯
陈传本
柏朋刚
陈济鸿
林蔚林
杨海松
HUANG Xingwu;FU Wankai;CHEN Chuanben;BAI Penggang;CHEN Jihong;LIN Weilin;YANG Haisong(Clinical Oncology School,Fujian Medical University,Fuzhou 350014,China;Radiotherapy Center,Fujian Cancer Hospital,Fuzhou 350014,China)
出处
《中国医学影像技术》
CSCD
北大核心
2023年第7期1070-1074,共5页
Chinese Journal of Medical Imaging Technology
基金
国家临床重点专科建设项目资助(2021)
福建省肿瘤放射与免疫治疗临床医学研究中心(2020Y2012)。
关键词
鼻咽肿瘤
循环生成对抗网络
体层摄影术
X线计算机
nasopharyngeal neoplasms
cycle-consistent generative adversarial networks
tomography,X-ray computed