Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction

  • Balamurali MurugesanEmail author
  • S. Vijaya Raghavan
  • Kaushik Sarveswaran
  • Keerthi Ram
  • Mohanasankar Sivaprakasam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)


Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images.


Magnetic Resonance Imaging (MRI) Reconstruction Global local networks Segmentation Deep learning Generative Adversarial Networks Cardiac MRI 


  1. 1.
    Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)CrossRefGoogle Scholar
  2. 2.
    Caballero, J., Bai, W., Price, A.N., Rueckert, D., Hajnal, J.V.: Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 106–113. Springer, Cham (2014). Scholar
  3. 3.
    Dedmari, M.A., Conjeti, S., Estrada, S., Ehses, P., Stöcker, T., Reuter, M.: Complex fully convolutional neural networks for MR image reconstruction. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 30–38. Springer, Cham (2018). Scholar
  4. 4.
    Hollingsworth, K.G.: Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Phys. Med. Biol. 60(21), R297–R322 (2015)CrossRefGoogle Scholar
  5. 5.
    Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 107:1–107:14 (2017)CrossRefGoogle Scholar
  6. 6.
    Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976, July 2017Google Scholar
  7. 7.
    Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21–45 (2019)CrossRefGoogle Scholar
  8. 8.
    Li, Z., Zhang, T., Zhang, D.: SEGAN: structure-enhanced generative adversarial network for compressed sensing MRI reconstruction. CoRR abs/1902.06455 (2019)Google Scholar
  9. 9.
    Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29(2), 102–127 (2019)CrossRefGoogle Scholar
  10. 10.
    Mardani, M., et al.: Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans. Med. Imaging 38(1), 167–179 (2019)CrossRefGoogle Scholar
  11. 11.
    Quan, T.M., Nguyen-Duc, T., Jeong, W.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imaging 37(6), 1488–1497 (2018)CrossRefGoogle Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  13. 13.
    Schlemper, J., et al.: Cardiac MR segmentation from undersampled k-space using deep latent representation learning. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 259–267. Springer, Cham (2018). Scholar
  14. 14.
    Wang, S., et al.: Accelerating magnetic resonance imaging via deep learning. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 514–517, April 2016Google Scholar
  15. 15.
    Yang, G., et al.: DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans. Med. Imaging 37(6), 1310–1321 (2018)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology Madras (IITM)ChennaiIndia
  2. 2.Healthcare Technology Innovation Centre (HTIC)IITMChennaiIndia

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