A deep recurrent neural network with primal-dual optimization for CT metal artifact reduction

Abstract

Metal Artifact Reduction (MAR) has been one of the most challenging problems in Computed Tomography (CT) imaging since the technology was invented 50 years ago. Metal implants in patient bodies prevent a CT system from accurately measuring patient anatomies because of the beam hardening effect, X-ray beams’ statistical property changes, and metallic objects’ shapes. Although this problem has been researched for many years, there are still limitations in the conventional model-based methods. Recently, many researchers started using Deep Learning (DL) for the CT MAR problem, which should work better than traditional methods because of its powerful representation capability. In this paper, a new DL-based method is proposed. Our technique is based upon one of the convex optimization methods called primal-dual optimization. A Recurrent Neural Network is adopted to implement the primal-dual optimization for CT MAR. Because of the multi-variable optimization, this neural network conducts dual-domain learning. In addition, the proposed method replaces a metal trace, commonly used in CT MAR methods, with a novel sinogram confidence map. This floating-point map works better than a binary metal trace because of its smoother boundary pixels. Results from extensive experiments indicate that our proposed method outperforms conventional model-based methods and DL-based methods in terms of the Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity (SSIM) as well as in terms of visual appearance.