  It has been recently shown that incorporating priori knowledge significantly
improves the performance of basic compressive sensing based approaches. We have
managed to successfully exploit this idea for recovering a matrix as a
summation of a Low-rank and a Sparse component from compressive measurements.
When applied to the problem of construction of 4D Cardiac MR image sequences in
real-time from highly under-sampled $k-$space data, our proposed method
achieves superior reconstruction quality compared to the other state-of-the-art
methods.
