LWCDNet: An Interpretable Learning Weighted Convolutional Dictionary Network for Metal Artifact Reduction in CT Images

In computed tomography (CT) imaging, highly attenuated metallic implants often result in strong metal artifacts, which significantly compromise image quality and pose considerable challenges for precise clinical diagnosis and effective treatment. Recently, deep learning (DL)-based approaches have shown promising results in metal artifact reduction (MAR) for CT images. Nonetheless, these approaches often address the problem as a broad image restoration challenge. The networks employed are generally constructed intuitively, lack interpretability, and struggle with metal artifacts of varying sizes. To alleviate this problem, we propose an innovative MAR model called the learning weighted convolutional dictionary network (LWCDNet). The LWCDNet architecture comprises multiple iterative blocks, with each iteration featuring two distinct modules dedicated to optimizing the tissue attenuation component and the metal artifact component. First, we advance traditional convolutional learning by introducing a reweighted variant and develop a learning weighted convolutional dictionary unit based on the iterative soft thresholding algorithm (ISTA). This approach is designed specifically for the representation of artifact features. Second, a lightweight residual network (ResNet) model attempts to improve the restoration quality of the tissue attenuation component. Finally, via a metal mask, we realize discriminative representation and constraints on different components in metal artifact CT images. Comprehensive experimental investigations are carried out to validate the efficacy of the proposed method. The results on both synthetic and clinical datasets indicate that LWCDNet significantly outperforms traditional and state-of-the-art MAR methods, effectively enhancing image quality.