Metal Artifact Reduction by Using Dual-Energy Raw Data Constraint Learning

Abstract

Computed tomography (CT) is of great significance in the field of medical diagnosis. However, metal artifacts in the reconstruction images are disadvantageous for doctors to make a fast and accurate diagnosis when high-density metals present in the scanned location. The spectral CT has excellent performance in metal artifact reduction (MAR) method, which can combinate the prior information can to realize the information complementarity. In this paper, a MAR method based on dual-energy raw data constrained learning is proposed in this paper. The raw projection data of high/low energy and the results of normalized metal artifact reduction (NMAR) are input to the dual-stream U-Net (DSU-Net) for getting the virtual monoenergetic image (VMI) to reduce the secondary artifacts. The experimental results show that the peak signal-to-noise ratio (PSNR) of the output image is up to 49.60, SSIM to 0.997. It is proved that the raw data constrained learning method can suppress the residual artifacts from the traditional information pretreatment method.