Due to the presence of metallic implants, imaging quality computed tomography (CT) would be heavily degraded. With rapid development deep learning, several neural network models have been proposed for metal artifact reduction (MAR). Since dual-domain MAR methods can leverage hybrid information from both sinogram and image domains, they significantly improved performance compared single domain m...

The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation radiation therapy. In this work, we present a novel deep-learning-based approach for artifact reduction (MAR). order to alleviate need anatomically identical image pairs (i.e., artifact-corrupted and artifact-free image) network...