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Cited by 1,630Image and Signal Denoising MethodsImage Enhancement TechniquesGenerative Adversarial Networks and Image Synthesis

Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss.

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Yang, Qingsong, Yan, Pingkun, Zhang, Yanbo, Yu, Hengyong, Shi, Yongyi, Mou, Xuanqin, Kalra, Mannudeep K., Zhang, Yi +2 more·2018·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2018.2827462PMC PMC6021013
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Abstract

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists' judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

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  • WGAN employs Wasserstein distance rather than Jensen-Shannon or Kullback-Leibler divergence to improve training performance.

    “This architecture employed Wasserstein distance to measure the distance between generated data with real data rather than Jensen–Shannon or Kullback–Leibler divergence to improve the training performance (Yang et al., 2018).”

    From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Yang et al., 2018

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Cited by

Sorted by citations

Authors
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., & Wang, G.
Affiliations
  • Rensselaer Polytechnic Institute (US)
  • University of Massachusetts Lowell (US)
  • Xi'an Jiaotong University (CN)
  • Harvard University (US)
  • Sichuan University (CN)
Corresponding author
Yan, Pingkun
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-06
Volume
37
Issue
6
Pages
1348-1357
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2018.2827462
PMC
PMC6021013
OpenAlex
W2743780012
Cited by
1,630
References
7
Citations by year
2026: 116 · 2025: 210 · 2024: 226 · 2023: 209 · 2022: 248 · 2021: 253 · 2020: 197 · 2019: 135 · 2018: 34 · 2014: 1
FWCI
77.52
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Natural Science Foundation of China (61671312)
  • National Institute of Biomedical Imaging and Bioengineering/National Institutes of Health (R01 EB016977, U01 EB017140)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
low-dose CT, CT image denoising, generative adversarial networks, Wasserstein distance, perceptual loss, image reconstruction, radiation dose reduction, perceptual features
MeSH terms
Deep Learning, Algorithms, Humans, Image Processing, Computer-Assisted, Radiation Dosage, Signal Processing, Computer-Assisted, Tomography, X-Ray Computed, Artifacts

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