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

<i>Objective.</i>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.<i>Approach.</i>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.<i>Main results.</i>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.<i>Significance.</i>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.