A Reweighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction

High-density implants such as metals often lead to serious artifacts in reconstructed computerized tomographic (CT) images, which hampers the accuracy of image-based diagnosis and treatment planning. In this paper, we propose a novel wavelet frame--based CT image reconstruction model to reduce metal artifacts. This model is built on a joint spatial and Radon (projection) domain (JSR) image reconstruction framework with a built-in weighting and reweighting mechanism in the Radon domain to repair degraded projection data. The new weighting strategy used in the proposed model makes the regularization in the Radon domain by wavelet frame transform more effective. The proposed model, which will be referred to as the reweighted JSR model, combines the ideas of the recently proposed wavelet frame--based JSR model [B. Dong, J. Li, and Z. Shen, J. Sci. Comput., 54 (2013), pp. 333--349] and the normalized metal artifact reduction model [E. Meyer, R. Raupach, M. Lell, B. Schmidt, and M. Kachelriess, Med. Phys., 37 (2010), pp. 5482--5493.] and manages to achieve noticeably better CT reconstruction quality than both methods. To solve the proposed reweighted JSR model, an efficient alternative iteration algorithm is proposed with guaranteed convergence. Numerical experiments on both simulated and real CT image data demonstrate the effectiveness of the reweighted JSR model and its advantage over some state-of-the-art methods.