Abstract
Both Compressed Sensing (CS) and parallel MRI (pMRI) techniques can accelerate MRI scans; the CS method by reducing the acquired dataset sizes and the pMRI method by acquiring simultaneously undersampled k-space data. In this paper, we relate CS to accelerated parallel imaging reconstruction. Medical images can have a sparser representation in a wavelet domain. We study in the first, the effect of various wavelet types on the reconstructions and we show then the performance of the CS-pMRI method using more advanced techniques L1-wavelet regularization to suppress noise in the reconstruction, in comparison with CS and pMRI methods.
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