A. G. Van-der-kolk, Clinical applications of 7T MRI in the brain, European journal of radiology, vol.82, pp.708-718, 2013.

C. Lazarus, 3D SPARKLING trajectories for highresolution T2*-weighted Magnetic Resonance imaging, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02067080

M. Lustig, D. Donoho, and J. M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, vol.58, pp.1182-1195, 2007.

A. Beck and M. Teboulle, A fast iterative shrinkagethresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.

D. Kim and J. A. Fessler, Adaptive restart of the optimized gradient method for convex optimization, In: Journal of Optimization Theory and Applications, vol.178, pp.240-263, 2018.

S. Boyd, Distributed optimization and statistical learning via the alternating direction method of multipliers, In: Found. and Trends in Mach. learn. 3, vol.1, pp.1-122, 2011.

L. Condat, A Primal-Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms, Journal of Optimization Theory and Applications, vol.158, pp.460-479, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00609728

M. Zaitsev, J. Maclaren, and M. Herbst, Motion artifacts in MRI: a complex problem with many partial solutions, Journal of Magnetic Resonance Imaging, vol.42, pp.887-901, 2015.

A. S. Health, Clinical Appropriateness Guidelines: Advanced Imaging, 2017.

K. A. Horvath, Real-time magnetic resonance imaging guidance for cardiovascular procedures, Seminars in thoracic and cardiovascular surgery, vol.19, pp.330-335, 2007.

J. Liang and C. Schönlieb, Faster FISTA". In: arXiv eprints, 2018.

S. Farrens, ModOpt: Modular Optimisation tools for solving inverse problems in Python, 2017.

C. Lazarus, SPARKLING: Novel non-Cartesian sampling schemes for accelerated 2D anatomical imaging at 7T using (a) 2D MRI phantom, p.512

, VD sampling (25% undersampling) (c) SPARKLING scheme (AF=15)

, True MR image and non-uniform k-space data used to benchmark algorithms. compressed sensing, 25th annual meeting of the International Society for Magnetic Resonance Imaging, 2017.

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Modeling & Simulation, vol.4, pp.1168-1200, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00017649

L. E. Gueddari, Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI, 10th IEEE Sensory Array and Multichannel (SAM) signal processing workshop, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01782428

J. A. Fessler, Optimization methods for MR image reconstruction, 2019.

A. Chambolle and C. , On the convergence of the iterates of" FISTA, Journal of Optimization Theory and Applications, vol.166, p.25, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01060130