Spread spectrum magnetic resonance imaging

Abstract

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

References in corpus (4)

• Sparsity and Incoherence in Compressive Sampling
• On Variable Density Compressive Sampling
• Compressed sensing for wide-field radio interferometric imaging
• Spread spectrum for imaging techniques in radio interferometry

Cited by in corpus (9)

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• Sparsity Averaging for Compressive Imaging
• Breaking the coherence barrier: A new theory for compressed sensing
• Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques
• Modulated Unit-Norm Tight Frames for Compressed Sensing
• Analyzing the structure of multidimensional compressed sensing problems through coherence
• Accelerate Single-shot Data Acquisitions Using Compressed Sensing and FRONSAC Imaging
• Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
• Compressive Imaging and Characterization of Sparse Light Deflection Maps