Recently, unsupervised learning is proposed to avoid the performance degrading caused by synthesized paired computed tomography (CT) images. However, existing unsupervised methods for metal artifact reduction (MAR) only use features in image space, which is not enough to restore regions heavily corrupted by metal artifacts. Besides, they lack the distinction and selection for effective features. To address these issues, we propose an attention-embedded decomposed network to reduce metal artifacts in both image space and sinogram space with unpaired images. Specifically, combining with the CT images prior, we decompose the artifact-affected images
to artifact images and content images. Besides, normal convolutions are embedded with attention design in pixel-wise and channel-wise to strengthen the representational capacity. Extensive experiments show notable improvements on both synthesized data and clinical data.