Metal artifact reduction(MAR) is one of the most important issues in Dental computed tomography
(CT). Various methods have been suggested for metal artifact removal, among which supervised learning
methods are most popular. However matched non-metal - and metal - real CT image pairs are dicult to
obtain. In this paper, we propose an unsupervised MAR method for CT using attention cycle-consistent
adversarial network. The proposed method is based on unsupervised learning scheme using adversarial
loss and cycle-consistency loss to overcome the none of paired data. Moreover adding the convolutional
block attention module (CBAM) layers, we can get more improved MAR image and preserve the detailed
texture of the original image compare to standard cycle-consistent adversarial network.