MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images
This addresses metal artifacts that interfere with diagnosis and processing in dental CBCT, but it is incremental as it builds on existing Transformer architectures with efficiency improvements.
The paper tackled metal artifact reduction in dental CBCT images by developing MARformer, an efficient Transformer that outperformed previous methods and other restoration Transformers in experiments on synthetic and real-world data.
Cone Beam Computed Tomography (CBCT) plays a key role in dental diagnosis and surgery. However, the metal teeth implants could bring annoying metal artifacts during the CBCT imaging process, interfering diagnosis and downstream processing such as tooth segmentation. In this paper, we develop an efficient Transformer to perform metal artifacts reduction (MAR) from dental CBCT images. The proposed MAR Transformer (MARformer) reduces computation complexity in the multihead self-attention by a new Dimension-Reduced Self-Attention (DRSA) module, based on that the CBCT images have globally similar structure. A Patch-wise Perceptive Feed Forward Network (P2FFN) is also proposed to perceive local image information for fine-grained restoration. Experimental results on CBCT images with synthetic and real-world metal artifacts show that our MARformer is efficient and outperforms previous MAR methods and two restoration Transformers.