We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s MRI, consists of premodulating the signal of interest by a linear chirp before random -space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s MRI performs better than state-of-the-art variable density -space under-sampling approaches.
['Gilles Puy', 'José P. Marques', 'Rolf Gruetter', 'Jean-Philippe Thiran', 'D. Van De Ville', 'Pierre Vandergheynst', 'Yves Wiaux']
Spread Spectrum Magnetic Resonance Imaging