论文丨Tao Wang, Hui Yu, Zhiwen Wang, Hu Chen, Yan Liu, Jingfeng Lu, and Yi Zhang*： SemiMAR: semi-supervised learning for CT metal artifact reduction

本文（SemiMAR: semi-supervised learning for CT metal artifact reduction）原载IEEE Journal of Biomedical and Health Informatics，由四川大学张意教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Metal artifacts lead to CT imaging quality degradation. With the success of deep learning (DL) in medical imaging, a number of DL-based supervised methods have been developed for metal artifact reduction (MAR). Nonetheless, fully-supervised MAR methods based on simulated data do not perform well on clinical data due to the domain gap. Although this problem can be avoided in an unsupervised way to a certain degree, severe artifacts cannot be well suppressed in clinical practice. Recently, semi-supervised metal artifact reduction (MAR) methods have gained wide attention due to their ability in narrowing the domain gap and improving MAR performance in clinical data. However, these methods typically require large model sizes, posing challenges for optimization. To address this issue, we propose a novel semi-supervised MAR framework. In our framework, only the artifact-free parts are learned, and the artifacts are inferred by subtracting these clean parts from the metal-corrupted CT images. Our approach leverages a single generator to execute all complex transformations, thereby reducing the model's scale and preventing overlap between clean part and artifacts. To recover more tissue details, we distill the knowledge from the advanced dual-domain MAR network into our model in both image domain and latent feature space. The latent space constraint is achieved via contrastive learning. We also evaluate the impact of different generator architectures by investigating several mainstream deep learning-based MAR backbones. Our experiments demonstrate that the proposed method competes favorably with several state-of-the-art semi-supervised MAR techniques in both qualitative and quantitative aspects. 

10.Tao Wang, Hui Yu, Zhiwen Wang, Hu Chen, Yan Liu, Jingfeng Lu, and Yi Zhang*. SemiMAR: semi-supervised learning for CT metal artifact reduction. IEEE Journal of Biomedical and Health Informatics, pp. 5369-5380, vol. 27, no. 11, 2023. （论文下载）