Artifact Region-Aware Transformer: Global Context Helps CT Metal Artifact Reduction

Due to the presence of metallic implants, the imaging quality of computed tomography (CT) is degraded by metal artifacts. Existing deep learning-based metal artifact reduction (MAR) methods focus on working locally within artifact and non-artifact regions, limiting in improving the performance of MAR, especially for large metal artifacts. To tackle the bottleneck, we propose a novel artifact region-aware transformer-based MAR network, dubbed MARFormer, which leverages non-artifact regions to aid in artifact region restoration. Specifically, we build an artifact mask estimation subnetwork to approximately identify artifact regions, while elaborate a local-global information interaction subnetwork consisting of a local information extraction module, an artifact region-aware attention module, and a channel attention fusion module (CAFM) to integrate local and global information. Under the guidance of the estimated artifact region, the proposed artifact region-aware attention module can effectively model the global context correlation between artifact and non-artifact regions. Additionally, the local and global information are adaptively fused using CAFM. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art MAR methods in terms of artifact reduction accuracy.