MUPO-Net: A Multilevel Dual-domain Progressive Enhancement Network with Embedded Attention for CT Metal Artifact Reduction

Abstract

Metal implants in patients cause severe streaking artifacts in computed tomography (CT) images, significantly compromising image quality. Deep learning methods have been successfully applied to metal artifact reduction (MAR) in CT, but often result in overly smooth images, failing to reconstruct complex details accurately. In this paper, we propose a multilevel dual-domain progressive enhancement network with embedded attention for MAR, termed MUPO-Net. Our approach constructs a Contrast Weight Mapping (CWM) module that generates a weighted heatmap, allocating weights to different regions based on the influence of metal artifacts, and an ASR-Net (Attention-Embedded Sinogram Restoration Network) that utilizes these weights to better remove artifacts in sinogram domain. Additionally, an Image Detail Enhancement Network (IDE-Net) is proposed to restore fine texture details in CT images through multi-scale feature fusion. Extensive experiments on both synthetic and clinical datasets demonstrate the superior effectiveness of MUPO-Net compared to the state-of-the-art MAR techniques.