Sorted Compressed Sensing in MRI

To improve the traditional Compressed Sensing (CS) framework for image reconstruction, we propose a CS technique with variable regularization parameter, which penalizes the pixels of the recovered image according to their magnitude. Herein, we present quantitative susceptibility map (QSM) reconstructions in in-vivo data, where the Sorted Compressed Sensing (SCS) technique produced results that demonstrate it is feasible to reconstruct high quality images. The proposed method produced gains up to 5-6 dB with respect of traditional CS.

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