Artifact Region-Aware Transformer: Global Context Helps CT Metal Artifact Reduction
Baoshun Shi, Shaolei Zhang, Fu ZhaoRan
Abstract
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.