M. Lustig, D. David, and M. S. Juan, Compressed sensing MRI, IEEE signal processing magazine, vol.25, issue.2, pp.72-82, 2008.

M. Lustig, D. David, and M. P. John, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic resonance in medicine, vol.58, issue.6, pp.1182-1195, 2007.

K. P. Pruessmann, M. Weiger, and M. B. Scheidegger, SENSE: sensi-tivity encoding for fast MRI, Magn Reson Med, vol.42, issue.5, pp.952-962, 1999.

. Ma, P. M. Griswold, R. M. Jakob, and . Heidemann, Generalized autocalibrating partially parallel acquisitions (GRAPPA), Magn Reson Med, vol.47, issue.6, pp.1202-1210, 2002.

Y. Wang and L. Ying, Compressed sensing dynamic cardiac cine MRI using learned spatiotemporal dictionary, IEEE transactions on Biomedical Engineering, vol.61, issue.4, pp.1109-1120, 2014.

K. T. Block, M. Uecker, and J. Frahm, Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint, Magnetic resonance in medicine, vol.57, issue.6, pp.1086-1098, 2007.

J. Yang, Y. Zhang, and W. Yin, A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.288-297, 2010.

L. Chaâri, J. C. Pesquet, and A. Benazza-benyahia, A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging, Medical image analysis, vol.15, issue.2, pp.85-201, 2011.

Z. Zhu, K. Wahid, and P. Baby, Compressed sensing-based MRI reconstruction using complex double-density dual-tree DWT, Journal of Biomedical Imaging, vol.5, p.10, 2013.

Z. Zhan, J. F. Cai, and D. Guo, Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction, IEEE Transactions on Biomedical Engineering, vol.63, issue.9, pp.1850-1861, 2016.

Y. Song, Z. Zhu, and Y. Lu, Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning, Magnetic resonance in medicine, vol.71, issue.3, pp.1285-1298, 2014.

S. Ramani, Z. Liu, and J. Rosen, Regularization parameter selection for nonlinear iterative image restoration and MRI reconstruction using GCV and SURE-based methods, IEEE Transactions on Image Processing, vol.21, issue.8, pp.3659-3672, 2012.

T. H. Chan, K. Jia, and S. Gao, PCANet: A simple deep learning baseline for image classification, IEEE Transactions on Image Processing, vol.24, issue.12, pp.5017-5032, 2015.

K. He, X. Zhang, and S. Ren, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

Y. Deng, Z. Ren, and Y. Kong, A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification, IEEE Transactions on Fuzzy Systems, vol.25, issue.4, pp.1006-1012, 2017.

Y. Kong, Y. Deng, and Q. Dai, Discriminative Clustering and Feature Selection for Brain MRI Segmentation, IEEE Signal Processing Letters, vol.22, issue.5, pp.573-577, 2015.

J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, vol.61, pp.85-117, 2015.

J. Mehta and A. Majumdar, RODEO: robust DE-aliasing autoencoder for real-time medical image reconstruction, Pattern Recognition, vol.63, pp.499-510, 2017.

Q. Zhu, B. Du, and B. Turkbey, Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect, Complexity, 2018.

Q. Zhu, B. Du, and B. Turkbey, Deeply-Supervised CNN for Prostate Segmentation, IEEE International Joint Conference on Neural Networks (IJCNN), pp.178-184, 2017.

Y. Yang, J. Sun, and H. Li, ADMM-Net: A deep learning approach for compressive sensing MRI, 2017.

K. Hammernik, T. Klatzer, and E. Kobler, Learning a variational network for reconstruction of accelerated MRI data, Magnetic resonance in medicine, vol.79, issue.6, 2017.

J. Schlemper, J. Caballero, and J. V. Hajnal, A deep cascade of convolutional neural networks for MR image reconstruction, International Conference on Information Processing in Medical Imaging, pp.647-658, 2017.

T. M. Quan, T. Nguyenduc, and . Jeong, Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss, IEEE Trans Med Imaging, vol.37, issue.6, pp.1488-1497, 2018.

G. Yang, S. Yu, and H. Dong, DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction, IEEE Trans. Med. Imaging, vol.37, issue.6, pp.1310-1321, 2018.

B. Zhu, J. Liu, and S. Cauley, Image reconstruction by domain-transform manifold learning, Nature, vol.555, issue.7697, p.487, 2018.

F. Knoll, K. Bredies, and T. Pock, Second order total generalized variation (TGV) for MRI, Magnetic resonance in medicine, vol.65, issue.2, pp.480-491, 2011.

X. Qu, D. Guo, and B. Ning, Undersampled MRI reconstruction with patch-based directional wavelets, Magnetic resonance imaging, vol.30, issue.7, pp.964-977, 2012.

S. Ravishankar and Y. Bresler, MR image reconstruction from highly undersampled k-space data by dictionary learning, IEEE transactions on medical imaging, vol.30, issue.5, pp.1028-1041, 2011.

J. Caballero, A. N. Price, and D. Rueckert, Dictionary learning and time sparsity for dynamic MR data reconstruction, IEEE transactions on medical imaging, vol.33, issue.4, pp.979-994, 2014.

T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems, SIAM journal on imaging sciences, vol.2, issue.2, pp.323-343, 2009.

M. Lustig, D. David, and M. P. John, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic resonance in medicine, vol.58, issue.6, pp.1182-1195, 2007.

S. Ravishankar and Y. Bresler, Data-driven learning of a union of sparsifying transforms model for blind compressed sensing, IEEE Transactions on Computational Imaging, vol.2, issue.3, pp.294-309, 2016.

H. Chen, Y. Zhang, and Y. Chen, Learned Experts' Assessment-based Reconstruction Network (LEARN) for Sparse-data CT, 2017.

D. Kingma and J. Ba, Adam: A method for stochastic optimization, Computer Science, 2014.

Y. Lecun, Efficient backprop, Neural networks: Tricks of the trade, vol.1524, pp.9-50, 1998.

J. Trzasko and A. Manduca, Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic l0-Minimization, IEEE Transactions on Medical imaging, vol.28, issue.1, p.2012, 2009.

, His current research interests including machine learning, MRI reconstruction and medical image processing, 2016.

, He is a professor of the LIST Laboratory and the Codirector of the CRIBs. His recent work concentrates on the image analysis, pattern recognition and fast algorithms of digital signal processing, Huazhong Shu (M'00-SM'06) received the B.S. degree in Applied Mathematics from Wuhan University, China, in 1987, and a Ph.D. degree in Numerical Analysis from the University of Rennes 1, 1992.

, Since 1986, he has been Director of Research at the National Institute for Health and Medical Research (INSERM), France, and since 1993 has been Professor at the New Jersey Institute of Technology, USA. He has been the Head of the Laboratoire Traitement du Signal et de l'Image, INSERM, up to 2003. His experience is related to 3D images, signal processing, pattern recognition, computational modeling and complex systems with applications in integrative biomedicine. He published more than 300 papers in journals and conferences and edited many books in these areas. He has served as the Editor-in-Chief of the, Jean Louis Coatrieux received the Ph.D. and State Doctorate in Sciences in 1973 and 1983, respectively, from the University of Rennes 1, 1996.