IEEE Access (Jan 2022)

Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising

  • Jaa-Yeon Lee,
  • Wonjin Kim,
  • Yebin Lee,
  • Ji-Yeon Lee,
  • Eunji Ko,
  • Jang-Hwan Choi

DOI
https://doi.org/10.1109/ACCESS.2022.3226510
Journal volume & issue
Vol. 10
pp. 126580 – 126592

Abstract

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Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based low-dose CT denoising algorithms were primarily based on supervised learning. However, supervised learning requires a large number of training samples, which is impractical in real-world scenarios. To address this problem, we propose a novel unsupervised domain adaptation approach for low-dose CT denoising. This proposed framework adapts the network pretrained with paired low- and normal-dose phantom images (source domain) to denoise unlabeled low-dose human CT images (target domain). Our framework modifies the action of the domain classifier, enabling the denoising network to be adapted to the target domain. Furthermore, we introduce a new backpropagation method, which we call domain-independent weighted backpropagation. By combining these techniques, we demonstrate that the denoising network can be properly trained without using clinical clean CT images. The experimental results showed that our method exhibited better performance in terms of both objective and perceptual image qualities when compared with current unsupervised denoising algorithms. Our proposed domain adaptation represents a first-use case in the context of CT denoising problems, with the possibility of extension to other image restoration tasks.

Keywords