Multi-perspective Adaptive Iteration Network for Metal Artifact Reduction

Metal artifact reduction (MAR) is important to alleviate the impacts of metal implants on clinical diagnosis with CT images. However, enhancing the quality of metal-corrupted image remains a challenge. Although the deep learning-based MAR methods have achieved impressive success, their interpretability and generalizability need further improvement. It is found that metal artifacts mainly concentrate in high frequency, and their distributions in the wavelet domain are significantly different from those in the image domain. Decomposing metal artifacts into different frequency bands is conducive for us to characterize them. Based on these observations, a model is constructed with dual-domain constraints to encode artifacts by utilizing wavelet transform. To facilitate the optimization of the model and improve its interpretability, a novel multi-perspective adaptive iteration network (MAIN) is proposed. Our MAIN is constructed under the guidance of the proximal gradient technique. Moreover, with the usage of the adaptive wavelet module, the network gains better generalization performance. Compared with the representative state-of-the-art deep learning-based MAR methods, the results show that our MAIN significantly outperforms other methods on both of a synthetic and a clinical datasets.