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In this paper we investigate an inverse reconstruction problem of Magnetic Resonance Imaging with few acquired body scanner samples. The missing information in the Fourier domain causes image artefacts, therefore iterative computationally expensive recovery techniques are needed. We propose a regularization approach based on second order derivative of both simulated and real images with highly undersampled data, obtaining a good reconstruction accuracy. Moreover, an accelerated regularizationdoi:10.1016/j.procs.2013.06.001 fatcat:olmjjgzgdjd25ko2md2763ysja