Abstract
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that enables different forms of generations and regularizations between the artifact-affected and artifact-free image domains to support unsupervised learning. Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset. When applied to clinical datasets, our method achieves considerable improvements over the supervised models. The source code of this paper is publicly available at https://github.com/liaohaofu/adn.
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Acknowledgement
This work was supported in part by NSF award #1722847 and the Morris K. Udall Center of Excellence in Parkinson’s Disease Research by NIH.
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Liao, H., Lin, WA., Yuan, J., Zhou, S.K., Luo, J. (2019). Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_23
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DOI: https://doi.org/10.1007/978-3-030-32226-7_23
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