CT metal artifact reduction based on the residual encoder-decoder network

Metal artifact reduction is of great significance to improve the clarity of CT images. In this domain, some problems include incomplete artifact removal and miss of organizational structure. To address these issues, proposes a method of metal artifact reduction based on the residual encoder-decoder network (RED-CNN-MAR). Firstly, the RED-CNN network is used to realize the end-to-end mapping from the metal artifact image to the metal artifact-free image. The BN layer is utilized after the convolutional layer to improve the training accuracy of the network. Meanwhile, the speed of convergence is enhanced. To integrate the advantages of different correction methods, the original image, linear-interpolation images and beam-hardingcorrection images are used as the three-channel input of the RED-CNN network. Secondly, the output image of the network is further processed in the projection domain. Finally, the corrected image without metal artifact is reconstructed by the filtering back projection algorithm. The RMSE of the image corrected by the RED-CNN-MAR method is reduced by 0.000 7. PSNR and SSIM are improved by 0.59 dB and 0.002 8, respectively. Experimental results show that the proposed method can effectively suppress metal artifactand reconstruct clear structural details. © 2020, Science Press. All right reserved.