• 卓越行动计划二期中文领军期刊
  • 中国科学引文数据库核心期刊
  • 中文核心期刊、中国科技核心期刊
  • 第1、2届国家期刊奖
  • 第3届国家期刊奖百种重点期刊奖
  • 中国精品科技期刊、中国百强报刊
  • 百种中国杰出学术期刊
LI Yuerong, WU Zhongke, WANG Xuesong, SHEN Jiali, WANG Xingce. WGAN for super-resolution megnatic resonance imaging[J]. Journal of Beijing Normal University(Natural Science), 2021, 57(6): 896-904. DOI: 10.12202/j.0476-0301.2021203
Citation: LI Yuerong, WU Zhongke, WANG Xuesong, SHEN Jiali, WANG Xingce. WGAN for super-resolution megnatic resonance imaging[J]. Journal of Beijing Normal University(Natural Science), 2021, 57(6): 896-904. DOI: 10.12202/j.0476-0301.2021203

WGAN for super-resolution megnatic resonance imaging

More Information
  • Received Date: April 19, 2021
  • Available Online: November 04, 2021
  • Magnetic resonance imaging (MRI), a tomography method, is widely used in clinical medicine for diagnosis of disease state.However, raw images often need to be further enhanced for better resolution.For super-resolution reconstruction, we propose a residual U-net WGAN back-sampling and super-sampling model, with perceptual, texture and adversarial loss function.A comparative experiment was conducted on 3000 MRI images, resulting in averaged PSNR of 33.09 and SSIM of 0.95, improving PSNR by 4.09 and SSIM by 0.06, proving the learning validity of low to high resolution images.This algorithm is stable, robust and could be widely used.
  • [1]
    曹辉,王绪本,苟量. 超声、磁共振和核医学成像的发展现状和趋势[J]. 成都理工大学学报(自然科学版),2002,29(2):232
    [2]
    KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics,Speech,and Signal Processing,1981,29(6):1153 doi: 10.1109/TASSP.1981.1163711
    [3]
    YANG J C, WRIGHT J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]// 2008 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2008, USA, AK, Anchorage: IEEE, 2008: 1
    [4]
    HUANG J B, SINGH A, AHUJA N. Single image super-resolution from transformed self-exemplars[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, USA, MA, Boston:IEEE, 2015: 5197
    [5]
    GLASNER D, BAGON S, IRANI M. Super-resolution from a single image[C]//2009 IEEE 12th International Conference on Computer Vision, September 29 -October 2, 2009, Japan, Kyoto:IEEE, 2009: 349
    [6]
    ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-image translation with conditional adversarial networks[EB/OL]. (2018-11-26)[2021-04-15].https://phillipi.github.io/pix2pix/
    [7]
    PARK S C,PARK M K,KANG M G. Super-resolution image reconstruction:a technical overview[J]. IEEE Signal Processing Magazine,2003,20(3):21 doi: 10.1109/MSP.2003.1203207
    [8]
    DONG C, LOY C C, HE K M, et al. Learning a deep convolutional network for image super-resolution[C]//European Conference on Computer Vision(ECCV 2014), Janury 01, 2014, Switzerland, Cham: Springer, 2014: 184
    [9]
    DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision(ECCV 2016), October 8, 2016, Switzerland, Cham: Springer, 2016: 391
    [10]
    SHI W Z, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, USA, NV, Las Vegas:IEEE, 2016: 1874
    [11]
    KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, USA, NV, Las Vegas: IEEE, 2016: 1646
    [12]
    HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, USA, NV, Las Vegas: IEEE, 2016: 770
    [13]
    LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C/OL].New York: IEEE, 2017[2021-04-15].https:/C.org/pdf/1707.02921.pdf
    [14]
    MAO X J, SHEN C H, YANG Y B. Image restoration using convolutional auto-encoders with symmetric skip connections[EB/OL]. arXiv, 2016,16(3):09056[2021-04-15].https://arxiv.org/abs/1606.08921
    [15]
    LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26,2017, HI, USA,Honolulu:IEEE, 2017: 105
    [16]
    WANG X T, YU K, WU S X, et al. Esrgan: enhanced super-resolution generative adversarial networks[EB/OL]. arXiv, 2018,18(2):09056[2021-04-15].https://arxiv.org/abs/1809.00219
    [17]
    HATVANI J,HORVÁTH A,MICHETTI J,et al. Deep learning-based super-resolution applied to dental computed tomography[J]. IEEE Transactions on Radiation and Plasma Medical Sciences,2018,3(2):120
    [18]
    RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention(MICCAI 2015),November 18,2015,Switzerland,Cham:Springer, 2015: 234
    [19]
    LECUN Y,BOTTOU L,BENGIO Y,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE,1998,86(11):2278 doi: 10.1109/5.726791
    [20]
    SZEGEDY G, LIU W, JIA Y Q, et al. Going deeper with convolutions[EB/OL]. arXiv, 2014,14(1):4842.https://arxiv.org/abs/1409.4842
    [21]
    GOODFELLOW I, POUGET-ABADIE, MIRZA M, et al. Generative adversarial nets[EB/OL]. arXiv, 2014,14(1): 2672[2021-04-15].https://arxiv.org/abs/1406.2661
    [22]
    RAN M S,HU J R,CHEN Y,et al. Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network[J]. Medical Image Analysis,2019,55:165 doi: 10.1016/j.media.2019.05.001
    [23]
    SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. arXiv, 2014,14(1):1409[2021-04-15].https://arxiv.org/abs/1409.1556v1
    [24]
    GATYS L A, ECKER A S, BETHGE M. Texture synthesis using convolutional neural networks[EB/OL]. arXiv, 2015,15(3): 262[2021-04-15].https://arxiv.org/pdf/1505.07376.pdf
  • Cited by

    Periodical cited type(0)

    Other cited types(4)

Catalog

    Article Metrics

    Article views (266) PDF downloads (41) Cited by(4)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return