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

Abstract

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 xray 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.

Joint reconstruction and low-rank decomposition for dynamic inverse problems

“…Here, one matrix has low-rank and models the stationary background over time and the other matrix is sparse to represent the temporal variation in space, which is expected to be sparse. Further low-rank approximation models are based on a trained principal component analysis used for cine cone-beam CT [22] or patch based low-rank regularisation terms [28]. Similar to our considered approaches is the work of [8], where a low-rank reconstruction method is used for the application to cine cone-beam CT, based on a matrix factorisation model and the assumption that only a few principle components are sufficient to reconstruct a given body motion of the patient.…”