An efficient interpolated compressed sensing method for highly correlated 2D multi-slice MRI

In 2D multi-slice magnetic resonance imaging (MRI), adjacent slices are normally highly correlated which can be exploited for non-uniform random undersampling. To compensate for undersampling, missing samples of intermediate slices may be estimated from their neighboring slices. In this paper, we propose an efficient two phase 2D multi-slice MR image reconstruction technique combining interpolation with wavelet tree sparsity based CS reconstruction from highly undersampled measurements. Simulation results show that the proposed method gives average improvements of 1.8–3.6 dB in reconstructions compared to the state-of-the-art within a clinically significant time.