Deep low-Rank plus sparse network for dynamic MR imaging

•A model-based unrolled low-rank plus sparse network (L+S-Net) for dynamic MR reconstruction is proposed.•A learned singular value thresholding (LSVT) was introduced to exploit the deep low rank prior in dynamic MR.•The iterative steps are unrolled into a network whose regularization parameters are learned during training instead of the empirical selection.•The convergence analysis guarantees the performance of the reconstruction theoretically.•Retrospective and prospective study on single-coil/multi-coil datasets show that the L+S-Net can improve the reconstruction result both qualitatively and quantitatively.