  Metal artifact reduction (MAR) is one of the most important research topics
in computed tomography (CT). With the advance of deep learning technology for
image reconstruction,various deep learning methods have been also suggested for
metal artifact removal, among which supervised learning methods are most
popular. However, matched non-metal and metal image pairs are difficult to
obtain in real CT acquisition. Recently, a promising unsupervised learning for
MAR was proposed using feature disentanglement, but the resulting network
architecture is complication and difficult to handle large size clinical
images. To address this, here we propose a much simpler and much effective
unsupervised MAR method for CT. The proposed method is based on a novel
beta-cycleGAN architecture derived from the optimal transport theory for
appropriate feature space disentanglement. Another important contribution is to
show that attention mechanism is the key element to effectively remove the
metal artifacts. Specifically, by adding the convolutional block attention
module (CBAM) layers with a proper disentanglement parameter, experimental
results confirm that we can get more improved MAR that preserves the detailed
texture of the original image.
