Low-dose limited view 4D CT reconstruction using patch-based low-rank regularization

Dynamic 4D x-ray computed tomography (CT) is often used for several applications such as respiratory and cardiac imaging. To acquire enough data for several phases, repeated x-ray CT scans should be conducted. Thus, the dose reduction is one of important issues for dynamic CT scans. Another issue is that insufficient angular sampling can be occurred by fast motions such as a cardiac CT imaging. In this paper, the main goal is to develop a patch based low rank regularization approach that exploits inherent similarities between dynamic images for reducing both noise and motion artifacts incurred by low dose and insufficient angular sampling, respectively. 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.