  We propose a novel compressed sensing technique to accelerate the magnetic
resonance imaging (MRI) acquisition process. The method, coined spread spectrum
MRI or simply s2MRI, consists of pre-modulating the signal of interest by a
linear chirp before random k-space under-sampling, and then reconstructing the
signal with non-linear 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 s2MRI performs better
than state-of-the-art variable density k-space under-sampling approaches
