Unsupervised CT Metal Artifact Learning Using Attention-Guided beta-CycleGAN

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Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning approaches for image reconstruction, various deep learning methods have been suggested for metal artifact reduction, among which supervised learning methods are most popular. However, matched metal-artifact-free and metal artifact corrupted 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 so complicated that it is difficult to handle large size clinical images. To address this, here we propose a simple and effective unsupervised learning method for MAR. The proposed method is based on a novel beta-cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Moreover, by adding the convolutional block attention module (CBAM) layers in the generator, we show that the metal artifacts can be more focused so that it can be effectively removed. Experimental results confirm that we can achieve improved metal artifact reduction that preserves the detailed texture of the original image.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-12
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.12, pp.3932 - 3944

ISSN
0278-0062
DOI
10.1109/TMI.2021.3101363
URI
http://hdl.handle.net/10203/290815
Appears in Collection
AI-Journal Papers(저널논문)
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