Computer Science ›› 2022, Vol. 49 ›› Issue (7): 113-119.doi: 10.11896/jsjkx.210600105

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Super-resolution Reconstruction of MRI Based on DNGAN

DAI Zhao-xia1, LI Jin-xin2, ZHANG Xiang-dong2, XU Xu3,4, MEI Lin3,4, ZHANG Liang3,5   

  1. 1 No.30 Institute of China Electronic Technology Corporation,Chengdu 610041,China
    2 College of Telecommunication Engineering,Xidian University,Xi'an 710071,China
    3 College of Computer Science and Technology,Xidian University,Xi'an 710071,China
    4 The Third Research Institute of Ministry of Public Security,Shanghai 200031,China
    5 Xi'an Key Laboratory of Intelligent Software Engineering(Xidian University),Xi'an 710071,China
  • Received:2021-06-12 Revised:2021-12-12 Online:2022-07-15 Published:2022-07-12
  • About author:DAI Zhao-xia,born in 1972,bachelor,senior engineer.Her main research interests include network information security and network management.
    ZHANG Liang,born in 1981,Ph.D,professor.His main research interests include robot and behavior identity.
  • Supported by:
    National Natural Science Foundation of China(62072358),National Key R&D Program of China(2020YFF0304900,2019YFB1311600) and Shanxi Province Key Research and Development Program(2018ZDXM-GY-036).

Abstract: 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.

Key words: Convolutional neural network, DenseNet, Generative adversarial network, Magnetic resonance imaging, Super-resolution reconstruction

CLC Number: 

  • TP391
[1]ZHANG L Y,WANG L,LIU Y J,et al.Study on micromecha-nical behavior of the proximal femur based on micro-magnetic resonance imaging and finite element analysis[J].Beijing Biomedical Engineering,2020,39(2):111-116.
[2]HIDENOBU M,MARINA H,YOSHINOBU Y,et al.Magnetic resonance imaging(MRI) and dynamic MRI evaluation of extranodal non-Hodgkin lymphoma in oral and maxillofacial regions[J].Oral Surgery,Oral Medicine,Oral Pathology and Oral Radiology,2019,113(1):126-33.
[3]LI J Q.Investigation of Several Methods in Magnetic Resonance Imaging[D].Shanghai:East China Normal University,2010.
[4]BATZ M,EICHENSEER A,SEILER J,et al.Hybird super-reso-lution combining example-based single-image and interpolation-based multi-image reconstruction approaches[C]//IEEE International Conference on Image Processing.2015:58-62.
[5]ZHANG H,ZHANG L,SHEN H.A blind super-resolution reconstruction method considering image registration errors[J].International Journal of Fuzzy Systems,2015,17(2):353-364.
[6]XIAO J S,LIU E Y,ZHU L,et al.Improved Image Super-Resolution Algorithm Based on Convolutional Neural Network[J].Acta Optica Sinica,2017(3):103-111.
[7]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Gene-rative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680.
[8]CHEN Y,FENG S,CHRISTODOULOU A G,et al.Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network[J].arXiv:1803.01417,2018.
[9]GAO Y,LIU Z,QIN P L,et al.Medical image super-resolution algorithm based on deep residual generative adversarial network[J].Journal of Computer Applications,2018(9):2689-2695.
[10]JIANG W H,LIU Z Y,LEE K H,et al.Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training[J].arXiv:1906.09745,2019.
[11]ZHAO H T,FANG H,WU X H,et al.K-space concept and its application in magnetic resonance imaging[J].Chinese J Med Imaging,1999,7(3):225-227.
[12]BRACEWELL R.The Fourier transform and its applications[M/OL].
[13]HUANG G,LIU Z,LAURENS V D M,et al.Densely Connec-ted Convolutional Networks[J].arXiv:1608.06993,2016.
[14]ZHAO Z C,LUO Z,WANG P Y,et al.Survey on Image Classification Algorithms Based on Deep Residual Network[J].Computer Systems & Applications,2020,29(1):14-21.
[15]GULRAJANI I,AHMED F,ARJOVSKY M,et al.ImprovedTraining of Wasserstein GANs[C]//Advances in Neural Information Processing Systems.2017:5767-5777.
[16]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].arXiv:1409.1556,2014.
[17]WANG Y,LI J,LU Y,et al.Image quality evaluation based on image weighted separating block peak signal to noise ratio[C]//International Conference on Neural Networks & Signal Proces-sing.IEEE,2003.
[18]ZHOU W,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Trans Image Process,2004,13(4):600-612.
[19]ZHANG A Z,LIU Z L,ZHOU X C,et al.Design of Image Sca-ling Engine Based Bicubic Interpolation Algorithm[J].Micro-electronics & Computer,2007(1):49-51.
[1] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[2] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[3] LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266.
[4] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[5] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[6] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[7] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[8] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[9] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[10] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[11] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[12] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
[13] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[14] XU Guo-ning, CHEN Yi-peng, CHEN Yi-ming, CHEN Jin-yin, WEN Hao. Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks [J]. Computer Science, 2022, 49(6A): 184-190.
[15] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
Full text



No Suggested Reading articles found!