Spread spectrum magnetic resonance imaging

TL;DR

This paper introduces s2MRI, a novel compressed sensing method for MRI that uses signal pre-modulation with a linear chirp to improve image reconstruction speed and quality, validated through simulations and experiments.

Contribution

The paper presents a new spread spectrum technique for MRI that enhances compressed sensing performance by optimizing coherence between sensing and sparsity bases.

Findings

s2MRI outperforms existing variable density sampling methods

The method improves reconstruction quality in numerical and real scans

Theoretical analysis supports the effectiveness of the approach

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