Abstract
Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images. Several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results were achieved, there is still much room to further improve performance. In this paper, a novel Dual-domain Adaptive-scaling Non-local Network (DAN-Net) is proposed for MAR. The corrupted sinogram was corrected using adaptive scaling first to preserve more tissue and bone details. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image is generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and non-local module are leveraged in the proposed network model. Experiments demonstrate the performance of the proposed DAN-Net is competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects. The code is available online: https://github.com/zjk1988/DAN-Net.
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Wang, T. et al. (2021). Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_24
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