Wavelet domain-based deep residual learning for metal artifact reduction in computed tomography

In this study, we proposed a wavelet domain-based deep residual learning strategy for reducing metal artifacts in computed tomography (CT) images. A fully-connected neural network (FCN) was constructed for learning the end-to-end non-linear mapping between the images including metal artifacts and the residual images. Training CT images were transformed into subband images using the 2D wavelet transformation for providing the high-frequency features during network training. The residual learning was implemented by using the subband images. The performance of the proposed technique was compared to that of the O-MAR algorithm. The results showed that metal artifacts were sufficiently suppressed by the proposed technique, and the proposed technique reduced the NRMSE by 12.34% and improved the SSIM by 0.84% compared to the O-MAR algorithm. In conclusion, the proposed model is able to efficiently reduce metal artifacts in CT images and has the superior performance compared with the commercial algorithm.