Sparsesense: Application of compressed sensing in parallel MRI
Compressed sensing (CS) has recently drawn great attentions in the MRI research community. The most desirable property of CS in MRI application is that it allows sampling of k-space well below Nyquist sampling rate, while still being able to reconstruct the image if certain conditions are satisfied. Recent work has successfully applied CS to reduce scanning time in conventional Fourier imaging. In this paper, the application of CS to parallel imaging, a fast imaging technique, is investigated to achieve an even higher imaging speed. The sampling scheme for incoherence is discussed and reconstruction method using Begman iteration is proposed. Our experiments show that the combined method, named SparseSENSE, can achieve a reduction factor higher than the number of channels.