Computed tomography (CT) utilizes X-ray technology for internal body imaging. However, the presence of metal objects often results in artifacts due to their significant absorption and scattering of X-rays, thus obstructing lesion diagnosis, especially in the presence of multiple metals. Existing artifact reduction methods often suffer from deficiencies in completeness and preservation of fine detail. To address this limitation, we propose a novel sinogram and image dual-domain network. Specifically, in the sinogram domain, two enhancement modules are designed: one for extracting information from regions affected by metal traces, and the other for learning to restore the sinogram corresponding to these metal traces. Subsequently, utilizing filtered back projection (FBP), artifact removal images are reconstructed in the image domain. Quantitative and qualitative analyses of synthetic images show our framework’s superiority over conventional Metal Artifact Reduction (MAR) methods in both synthetic and clinical settings.