  The existence of metallic implants in projection images for cone-beam
computed tomography (CBCT) introduces undesired artifacts which degrade the
quality of reconstructed images. In order to reduce metal artifacts, projection
inpainting is an essential step in many metal artifact reduction algorithms. In
this work, a hybrid network combining the shift window (Swin) vision
transformer (ViT) and a convolutional neural network is proposed as a baseline
network for the inpainting task. To incorporate metal information for the Swin
ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding
methods are investigated. Both methods have improved the performance of the
baseline network. Furthermore, by choosing appropriate window size, the model
with neighborhood-embedding could achieve the lowest mean absolute error of
0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in
CBCT projections. At the end, the efficiency of metal-conscious embedding on
both simulated and real cadaver CBCT data has been demonstrated, where the
inpainting capability of the baseline network has been enhanced.
