Reconstruction of dynamic MRI based on RPCA model

In this paper, we propose a method that reconstruct the dynamic MRI from partial measurements by using a low-rank plus sparse regularization prior. It separated the dynamic MRI as a static background and dynamic organizations by robust principal component analysis. The minimization problem is solved by the alternating direction methods of multipliers (ADMM). The method is validated with numerical phantom simulations and cardiac MRI data against a direct Fourier inversion and k-t SLR algorithm. Results indicate this method improved the imaging quality with low computational time.