K. Pruessmann, M. Weiger, M. Scheidegger, and P. Boesiger, SENSE: Sensitivity encoding for fast MRI, Magnetic Resonance in Medicine, vol.30, issue.5, pp.952-962, 1999.
DOI : 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S

M. Griswold, . Jakob, M. Rm-heidemann, V. Nittka, J. Jellus et al., Generalized autocalibrating partially parallel acquisitions (GRAPPA), Generalized autocalibrating partially parallel acquisitions (GRAPPA), pp.1202-1210, 2002.
DOI : 10.1002/(SICI)1522-2594(199906)41:6<1236::AID-MRM21>3.0.CO;2-T

I. Y. Chun, B. Adcock, and T. M. Talavage, Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion, IEEE Transactions on Medical Imaging, vol.35, issue.1, pp.354-368, 2016.
DOI : 10.1109/TMI.2015.2474383

D. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

E. J. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.
DOI : 10.1109/TIT.2005.862083

M. Lustig, D. Donoho, and J. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, vol.170, issue.6, pp.1182-1195, 2007.
DOI : 10.1002/mrm.21391

G. Puy, P. Vandergheynst, and Y. Wiaux, On Variable Density Compressive Sampling, IEEE Signal Processing Letters, vol.18, issue.10, pp.595-598, 2011.
DOI : 10.1109/LSP.2011.2163712

N. Chauffert, P. Ciuciu, and P. Weiss, Variable density compressed sensing in MRI. Theoretical vs heuristic sampling strategies, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.298-301, 2013.
DOI : 10.1109/ISBI.2013.6556471
URL : https://hal.archives-ouvertes.fr/hal-00848271

N. Chauffert, P. Ciuciu, J. Kahn, and P. Weiss, Variable Density Sampling with Continuous Trajectories, SIAM Journal on Imaging Sciences, vol.7, issue.4, pp.1962-1992, 2014.
DOI : 10.1137/130946642
URL : https://hal.archives-ouvertes.fr/hal-00908486

G. Glover and J. Pauly, Projection Reconstruction Techniques for Reduction of Motion Effects in MRI, Magnetic Resonance in Medicine, vol.19, issue.2, pp.275-289, 1992.
DOI : 10.1148/radiology.181.1.1909445

C. Meyer, B. Hu, D. Nishimura, and A. Macovski, Fast Spiral Coronary Artery Imaging, Magnetic Resonance in Medicine, vol.25, issue.2, pp.202-213, 1992.
DOI : 10.2214/ajr.149.2.245

J. Keiner, S. Kunis, and D. Potts, Using NFFT 3---A Software Library for Various Nonequispaced Fast Fourier Transforms, ACM Transactions on Mathematical Software, vol.36, issue.4, p.19, 2009.
DOI : 10.1145/1555386.1555388

J. Fessler and B. Sutton, Nonuniform fast fourier transforms using min-max interpolation, IEEE Transactions on Signal Processing, vol.51, issue.2, pp.560-574, 2003.
DOI : 10.1109/TSP.2002.807005

M. Guerquin-kern, M. Haberlin, K. Pruessmann, and M. Unser, A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, vol.30, issue.9, pp.1649-1660, 2011.
DOI : 10.1109/TMI.2011.2140121
URL : https://hal.archives-ouvertes.fr/hal-01813870

L. Chaari, J. Pesquet, A. Benazza-benyahia, and P. Ciuciu, A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging???, Medical Image Analysis, vol.15, issue.2, pp.185-201, 2011.
DOI : 10.1016/j.media.2010.08.001
URL : https://hal.archives-ouvertes.fr/hal-00692260

B. Zhao, J. Haldar, A. Christodoulou, and Z. Liang, )-space data with joint partial separability and sparsity constraints, IEEE Transactions on Medical Imaging, vol.31, issue.9, pp.1809-1820, 2012.
DOI : 10.1109/TMI.2012.2203921

J. Haldar, Low-Rank Modeling of Local <formula formulatype="inline"> <tex Notation="TeX">$k$</tex></formula>-Space Neighborhoods (LORAKS) for Constrained MRI, IEEE Transactions on Medical Imaging, vol.33, issue.3, pp.668-681, 2014.
DOI : 10.1109/TMI.2013.2293974

B. Adcock, C. Hansen, B. Poon, and . Roman, BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING, Forum of Mathematics, Sigma, vol.94840, 2017.
DOI : 10.1017/S0962492900002816

C. Lazarus, P. Weiss, A. Vignaud, and P. Ciuciu, An empirical study of the maximum degree of acceleration in Compressed Sensing MRI, Magn. Reson. Med., CEA/NeuroSpin & INRIA Saclay Parietal, 2017.

D. Liang, B. Liu, J. Wang, and L. Ying, Accelerating SENSE using compressed sensing, Magnetic Resonance in Medicine, vol.51, issue.6, pp.1574-1584, 2009.
DOI : 10.1137/1.9781611970104

]. F. Huang, Y. Chen, W. Yin, W. Lin, X. Ye et al., A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: Self-feeding sparse SENSE, Magnetic Resonance in Medicine, vol.58, issue.4, pp.1078-1088, 2010.
DOI : 10.4310/CMS.2010.v8.n1.a6

A. Taylor, J. Hendrickx, and F. Glineur, Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization, Journal of Optimization Theory and Applications, vol.39, issue.1, 2017.
DOI : 10.1109/CDC.2017.8263832

L. Patrick, . Combettes, R. Valérie, and . Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.

A. Beck and M. Teboulle, Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems, IEEE Transactions on Image Processing, vol.18, issue.11, pp.2419-2434, 2009.
DOI : 10.1109/TIP.2009.2028250

M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad et al., ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA, Magnetic Resonance in Medicine, vol.43, issue.3, pp.990-1001, 2014.
DOI : 10.1002/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G

E. Yeh, M. Stuber, . Mckenzie, T. Botnar, M. Leiner et al., Inherently self-calibrating non-cartesian parallel imaging, Magnetic Resonance in Medicine, vol.48, issue.1, pp.1-8, 2005.
DOI : 10.1002/mrm.20517

A. Chambolle, On a class of first-order algorithms for convex problems with applications to imaging, Isis, pp.1-49, 2010.

C. Bng and . V?uv?u, A splitting algorithm for dual monotone inclusions involving cocoercive operators, Advances in Computational Mathematics, vol.38, issue.3, pp.667-681, 2013.

L. Condat, A Primal???Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms, Journal of Optimization Theory and Applications, vol.23, issue.1???2, pp.460-479, 2013.
DOI : 10.1081/NFA-120003674
URL : https://hal.archives-ouvertes.fr/hal-00609728

S. Ramani, Z. Liu, J. Rosen, J. Nielsen, and . Fessler, 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.
DOI : 10.1109/TIP.2012.2195015
URL : http://europepmc.org/articles/pmc3411925?pdf=render

C. Lazarus, P. Weiss, N. Chauffert, F. Mauconduit, M. Bottlaender et al., SPARKLING: Novel Non-Cartesian Sampling Schemes for Accelerated 2D Anatomical Imaging at 7T Using Compressed Sensing, 25th annual meeting of the ISMRM, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01577200

P. Combettes and J. Pesquet, Proximal splitting methods in signal processing, " in Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.
DOI : 10.1007/978-1-4419-9569-8_10
URL : http://www.ann.jussieu.fr/%7Eplc/prox.pdf

Y. Nesterov, Gradient methods for minimizing composite objective function, 2007.
DOI : 10.1007/s10107-012-0629-5

C. Boyer, N. Chauffert, P. Ciuciu, J. Kahn, and P. Weiss, On the Generation of Sampling Schemes for Magnetic Resonance Imaging, SIAM Journal on Imaging Sciences, vol.9, issue.4, pp.2039-2072, 2016.
DOI : 10.1137/16M1059205
URL : https://hal.archives-ouvertes.fr/hal-01373758

Z. Wang, H. Bovik, E. Sheikh, and . Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861
URL : http://www.cns.nyu.edu/~zwang/files/papers/ssim.pdf

M. Uecker, F. Ong, J. Tamir, D. Bahri, P. Virtue et al., Berkeley advanced reconstruction toolbox, Proc. Intl. Soc. Mag. Reson. Med, p.2486, 2015.