Cardiac computed tomography (CT) is widely used in diagnosis for the coronary artery heart disease. Due to the heart motion, ECG gating is usually applied to the cardiac CT. Although diastolic phase has mostly enough angular data, the systolic phase suffers from insufficient angular data. Hence, the main goal of this paper is to develop a patch based low rank regularization approach that exploits inherent similarities between diastolic and systolic images for reducing artifacts in systolic image. In particular, our optimization framework is based on the Poisson log-likelihood and the non-convex low rank regularization. To address the non-separable and non-convex optimization problem, we used the separable paraboloidal surrogates (SPS) for Poisson log-likelihood, and the concave-convex procedure (CCCP) for non-convex low rank regularization, which guarantees the fast convergence. We confirm that the proposed algorithm can provide significantly improved image quality.