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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2311.09590 (eess)
[Submitted on 16 Nov 2023 (v1), last revised 18 Apr 2024 (this version, v2)]

Title:MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images

Authors:Yuxuan Shi, Jun Xu, Dinggang Shen
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Abstract: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.
Comments: under consideration of Computer Vision and Image Understanding journal
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2311.09590 [eess.IV]
  (or arXiv:2311.09590v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2311.09590

Submission history

From: Yuxuan Shi [view email]
[v1] Thu, 16 Nov 2023 06:02:03 UTC (3,491 KB)
[v2] Thu, 18 Apr 2024 08:49:03 UTC (3,300 KB)
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