  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.
