循环生成对抗网络基于颅脑MR图生成伪CT图模型  

Generating pseudo CT images based on brain MR images model using cycle-consistent generative adversarial network

作　　者：奚谦逸 张钒 李奇轩 焦竹青[1,3] 倪昕晔 XI Qianyi;ZHANG Fan;LI Qixuan;JIAO Zhuqing;NI Xinye(School of Microelectronics and Control Engineering,Changzhou University,Changzhou 213164,China;Department of Radiotherapy,the Second People's Hospital of Changzhou Affiliated to Nanjing Medical University,Changzhou 213003,China;Central Laboratory of Medical Physics,Nanjing Medical University,Changzhou 213003,China)

机构地区：[1]常州大学微电子与控制工程学院,江苏常州213164 [2]南京医科大学附属常州第二人民医院放疗科,江苏常州213003 [3]南京医科大学医学物理研究中心,江苏常州213003

出　　处：《中国医学影像技术》2023年第2期264-269,共6页Chinese Journal of Medical Imaging Technology

基　　金：江苏省重点研发计划社会发展项目(BE2022720);江苏省卫生健康委医学科研立项面上项目(M2020006)。

摘　　要：目的采用改进循环生成对抗网络(UCycleGAN)基于颅脑MR图映射模型生成伪CT图。方法对50例鼻咽癌颅脑MR图与CT图进行配准及预处理;以U-net网络并添加L1距离函数替换原始循环GAN(CycleGAN)模型生成器的深度残差网络。随机选取40例图像作为训练数据对UCycleGAN模型进行训练,将剩余10例用于测试;比较生成伪CT图与原始图像质量的差异,并与以ResNet、U-net的CycleGAN以及Pix2Pix生成的图像进行对比。结果相比其他模型,以UCycleGAN模型生成的伪CT图与原始CT图更为接近,体素平均绝对误差(MAE)为(81.45±3.87)HU,峰值信噪比(PSNR)为(34.13±3.28)dB,结构相似性(SSIM)为0.87±0.03。采用UCycleGAN模型生成的伪CT图的MAE小于、而SSIM明显大于其他3种模型(P均<0.05);UCycleGAN伪CT图的PSNR大于CycleGAN_ResNet图像(P<0.05)。结论利用UCycleGAN可基于颅脑MR图生成伪CT图;改良后CycleGAN模型的准确性更高。Objective To establish brain MR images mapping models for generating pseudo CT images based on cycle-consistent generative adversarial network(UCycleGAN).Methods Brain MR images and CT images of 50 patients with nasopharyngeal carcinoma were registered and preprocessed.The depth residual network of the original cycle-consistent GAN(CycleGAN)model generator were replaced with U-net network,and L1 distance function was added.Images of 40 cases were randomly selected as training data to train the UCycleGAN model,while of the remaining 10 cases were used for testing.Imaging qualities were compared between generated pseudo CT and original CT,also among CycleGAN with ResNet,U-net and Pix2Pix.Results Compared with images obtained with other models,pseudo CT image generated with UCycleGAN model was more closer to original CT image,its average absolute error(MAE)of voxels was(81.45±3.87)HU,the peak signal-to-noise ratio(PSNR)was(34.13±3.28)dB,and the structural similarity(SSIM)was 0.87±0.03.MAE of pseudo CT images generated with UCycleGAN model were less,while SSIM were significantly larger than that of the other three models(all P<0.05),and PSNR of UCycleGAN pseudo CT images were greater than that of CycleGAN_ResNet(P<0.05).Conclusion Using UCycleGAN could generate pseudo CT images based on brain MR images,and the accuracy might be improved with CycleGAN model.

关 键 词：脑肿瘤 放射治疗 循环生成对抗网络 磁共振成像 

分 类 号：R742[医药卫生—神经病学与精神病学] R445[医药卫生—临床医学]