基于DNGAN的磁共振图像超分辨率重建算法  

Super-resolution Reconstruction of MRI Based on DNGAN

作　　者：戴朝霞[1] 李锦欣 张向东[2] 徐旭 梅林[3,4] 张亮 DAI Zhao-xia;LI Jin-xin;ZHANG Xiang-dong;XU Xu;MEI Lin;ZHANG Liang(No.30 Institute of China Electronic Technology Corporation,Chengdu 610041,China;College of Telecommunication Engineering,Xidian University,Xi'an 710071,China;College of Computer Science and Technology,Xidian University,Xi'an 710071,China;The Third Research Institute of Ministry of Public Security,Shanghai 200031,China;Xi'an Key Laboratory of Intelligent Software Engineering(Xidian University),Xi'an 710071,China)

机构地区：[1]中国电子科技集团公司第三十研究所,成都610041 [2]西安电子科技大学通信工程学院,西安710071 [3]西安电子科技大学计算机科学与技术学院,西安710071 [4]公安部第三研究所,上海200031 [5]西安市智能软件工程重点实验室(西安电子科技大学),西安710071

出　　处：《计算机科学》2022年第7期113-119,共7页Computer Science

基　　金：国家自然科学基金(62072358);国家重点研发计划(2020YFF0304900,2019YFB1311600);陕西省重点研发计划(2018ZDXM-GY-036)。

摘　　要：磁共振图像的质量会影响医生对患者身体情况的判断,高清晰度的磁共振图像更有利于医生做出准确的诊断。利用计算机技术对磁共振图像进行超分辨率重建,可以由低分辨率的磁共振图像得到高分辨率的磁共振图像。基于生成对抗网络强大的生成能力及其非监督学习特性,文中研究了基于生成对抗网络的磁共振图像超分辨率算法,设计了一个结合残差网络结构及DenseNet结构作为生成网络的网络模型DNGAN。该网络使用WGAN-GP理论作为对抗损失来稳定生成对抗网络的训练。除此之外,使用内容损失函数以及感知损失函数作为网络的损失函数。同时,为了更好地利用磁共振图像丰富的频域信息,将磁共振图像的频域信息作为频域损失函数添加到网络中。为了证明DNGAN模型的有效性,将其磁共振图像超分辨率实验结果与SRGAN以及双三次插值法的磁共振图像超分辨率重建结果进行对比,表明DNGAN模型能够有效地对磁共振图像进行超分辨率重建。The quality of MRI will affect doctor's judgment on patient's physical conditions,and the high-resolution MRI is more conducive to doctor to make an accurate diagnosis.Using computer technology to perform super-resolution reconstruction of MRI can obtain high-resolution MRI from existing low-resolution MRI.Based on the strong generation ability of the generative adversarial networks and the unsupervised learning characteristics of the generative adversarial networks,this paper studies the MRI super-resolution algorithm based on the generative adversarial networks.It designs a generative adversarial network model DNGAN that combines ResNet structure and DenseNet structure.In this network,the WGAN-GP theory is used as the adversarial loss to stabilize the training of the generative adversarial networks.In addition,the content loss function and the perceptual loss function are also used as the loss function of the network.At the same time,in order to make better use of the rich frequency domain information of MRI,the frequency domain information of MRI is added to the network as a frequency domain loss function.In order to prove the effectiveness of DNGAN,the MRI super-resolution experimental results of DNGAN are compared with that of SRGAN and bicubic interpolation method.Experimental results show that DNGAN model can effectively perform super-resolution reconstruction of MRI.

关 键 词：超分辨率重建 生成对抗网络 磁共振图像 卷积神经网络 DenseNet 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]