Accelerated Magnetic Resonance Imaging by Adversarial Neural Network

  • Ohad ShitritEmail author
  • Tammy Riklin Raviv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


A main challenge in Magnetic Resonance Imaging (MRI) for clinical applications is speeding up scan time. Beyond the improvement of patient experience and the reduction of operational costs, faster scans are essential for time-sensitive imaging, where target movement is unavoidable, yet must be significantly lessened, e.g., fetal MRI, cardiac cine, and lungs imaging. Moreover, short scan time can enhance temporal resolution in dynamic scans, such as functional MRI or dynamic contrast enhanced MRI. Current imaging methods facilitate MRI acquisition at the price of lower spatial resolution and costly hardware solutions.

We introduce a practical, software-only framework, based on deep learning, for accelerating MRI scan time allows maintaining good quality imaging. This is accomplished by partial MRI sampling, while using an adversarial neural network to estimate the missing samples. The inter-play between the generator and the discriminator networks enables the introduction of an adversarial cost in addition to a fidelity loss used for optimizing the peak signal-to-noise ratio (PSNR). Promising image reconstruction results are obtained for 1.5T MRI where only 52% of the original data are used.



This research is partially supported by the Israel Science Foundation (T.R.R. 1638/16) and the IDF Medical Corps (T.R.R.).


  1. 1.
    Bhatia, K.K., Caballero, J., Price, A.N., Sun, Y., Hajnal, J.V., Rueckert, D.: Fast reconstruction of accelerated dynamic MRI using manifold kernel regression. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 510–518. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_61 CrossRefGoogle Scholar
  2. 2.
    Caballero, J., Price, A.N., Rueckert, D., Hajnal, J.V.: Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans. Med. Imaging 33(4), 979–994 (2014)CrossRefGoogle Scholar
  3. 3.
    Deshmane, A., Gulani, V., Griswold, M.A., Seiberlich, N.: Parallel MR imaging. J. Magn. Reson. Imaging 36(1), 55–72 (2012)CrossRefGoogle Scholar
  4. 4.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)Google Scholar
  6. 6.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  7. 7.
    Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A.: Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002)CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)CrossRefGoogle Scholar
  10. 10.
    Moeller, S., Yacoub, E., Olman, C.A., Auerbach, E., Strupp, J., Harel, N., Uğurbil, K.: Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63(5), 1144–1153 (2010)CrossRefGoogle Scholar
  11. 11.
    Nie, D., Trullo, R., Petitjean, C., Ruan, S., Shen, D.: Medical image synthesis with context-aware generative adversarial networks. arXiv preprint arXiv:1612.05362 (2016)
  12. 12.
    Oktay, O., et al.: Multi-input cardiac image super-resolution using convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 246–254. Springer, Cham (2016). doi: 10.1007/978-3-319-46726-9_29 CrossRefGoogle Scholar
  13. 13.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)Google Scholar
  14. 14.
    Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P., et al.: Sense: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952–962 (1999)CrossRefGoogle Scholar
  15. 15.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  16. 16.
    Ravishankar, S., Bresler, Y.: MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans. Med. Imaging 30(5), 1028–1041 (2011)CrossRefGoogle Scholar
  17. 17.
    Roemer, P.B., Edelstein, W.A., Hayes, C.E., Souza, S.P., Mueller, O.: The NMR phased array. Magn. Reson. Med. 16(2), 192–225 (1990)CrossRefGoogle Scholar
  18. 18.
    Usman, M., Vaillant, G., Atkinson, D., Schaeffter, T., Prieto, C.: Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magn. Reson. Med. 72(4), 1130–1140 (2014)CrossRefGoogle Scholar
  19. 19.
    Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F., Feng, D., Liang, D.: Accelerating magnetic resonance imaging via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 514–517. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe Zlotowski Center for Neuroscience, Ben-Gurion University of the NegevBeershebaIsrael

Personalised recommendations