MAReraser: Metal Artifact Reduction with Image Prior Using CNN and Transformer Together

This paper presents a new dual domain network with image prior based on Convolutional Neural Network (CNN) and Transformer simultaneously for CT Metal Artifact Reduction (MAR). Challenges in MAR derive from the following aspects: firstly, the different morphologies of metal artifacts complexify resolving the issue just in a single domain; secondly, albeit many methods excel in quantitative metrics, yet the restored anatomical structures are over-smooth blurring reconstructed CT images; thirdly, MAR demands better performance as a clinical application, but the approaches relying on CNN or Transformer struggle due to CNN’s restricted spatial scope and Transformer’s ignorance to the local details, respectively, that is, CNN focuses on the local information while Transformer emphasizes the global information with higher computational complexity. To address these problems, we put forward MAReraser, a novel dual domain network, to deal with metal artifacts. MAReraser removes metal artifacts in both the projection and image domains, effectively reducing heteromorphic metal artifacts. Moreover, MAReraser introduces the image prior generated by an image prior subnet to refine the quality of reconstructed CT images. The prior subnet is pretrained in an expanded dataset which incorporates CT images corrected by diverse traditional MAR methods, providing extra potential prior knowledge from different perspectives. Further, the network backbone of MAReraser integrates CNN and Transformer, enabling complementary local and global feature extraction and balancing computational complexity. Extensive experiment results demonstrate that our method outperforms several other approaches whether in quantitative metrics or in qualitative visualization results.