mlsanityData to 14 Oct 2026

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

arXiv 2311.09590 ↗First posted 16 Nov 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 44 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

22 of 44 references.

References · 22

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10 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reductioncited by 0
  2. 2026Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reductioncited by 0
  3. 2026Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networkscited by 0