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.
@article{arxiv.2311.09590,
title = {MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images},
author = {Yuxuan Shi and Jun Xu and Dinggang Shen},
journal= {arXiv preprint arXiv:2311.09590},
year = {2024}
}
Comments
under consideration of Computer Vision and Image Understanding journal