Metal artifacts correction based on a physics-informed nonlinear sinogram completion model

Abstract Objective. Metal artifacts seriously deteriorate CT image quality. Current metal artifacts reduction (MAR) methods suffer from insufficient correction or easily introduce secondary artifacts. To better suppress metal artifacts, we propose a sinogram completion approach extracting and utilizing useful information that contained in the corrupted metal trace projections. Approach. Our method mainly contains two stages: sinogram interpolation by an improved normalization technique for initial correction and physics-informed nonlinear sinogram decomposition for further improvement. In the first stage, different from the popular normalized metal artifact reduction method, we propose a more meaningful normalization scheme for the interpolation procedure. In the second stage, instead of performing a linear sinogram decomposition as done in the physics-informed sinogram completion method, we introduce a nonlinear decomposition model that can accurately separate the sinogram into metal and non-metal contributions by better modeling the physical scanning process. The interpolated sinogram and physics-informed correction compensate each other to reach the optimal correction results. Main results. Experimental results on simulated and real data indicate that, in terms of both structures preservation and detail recovery, the proposed physics-informed nonlinear sinogram completion method achieves very competitive performance for MAR compared to existing methods. Significance. According to our knowledge, it is for the first time that a nonlinear sinogram decomposition model is proposed in the literature for metal artifacts correction. It might motivate further research exploring this idea for various sinogram processing tasks.