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Window-Level is a Strong Denoising Surrogate

arXiv 2105.07153 ↗First posted 15 May 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

CT image quality is heavily reliant on radiation dose, which causes a trade-off between radiation dose and image quality that affects the subsequent image-based diagnostic performance. However, high radiation can be harmful to both patients and operators. Several (deep learning-based) approaches have been attempted to denoise low dose images. However, those approaches require access to large training sets, specifically the full dose CT images for reference, which can often be difficult to obtain. Self-supervised learning is an emerging alternative for lowering the reference data requirement facilitating unsupervised learning. Currently available self-supervised CT denoising works are either dependent on foreign domain or pretexts are not very task-relevant. To tackle the aforementioned challenges, we propose a novel self-supervised learning approach, namely Self-Supervised Window-Leveling for Image DeNoising (SSWL-IDN), leveraging an innovative, task-relevant, simple, yet effective surrogate -- prediction of the window-leveled equivalent. SSWL-IDN leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework. Our extensive (in- and cross-domain) experimentation demonstrates the effectiveness of SSWL-IDN in aggressive denoising of CT (abdomen and chest) images acquired at 5% dose level only.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 15 May 2021.

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17 of 29 references.

References · 17

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  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
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  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
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  7. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
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  9. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
  10. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  11. 2019Variational Denoising Network: Toward Blind Noise Modeling and Removalcited by 117
  12. 2020Noise2Same: Optimizing A Self-Supervised Bound for Image Denoisingcited by 56
5 more
  1. 2015Denoising Criterion for Variational Auto-Encoding Frameworkcited by 43
  2. 2017Sharpness-aware Low dose CT denoising using conditional generative adversarial networkcited by 28
  3. 2016Low-dose CT denoising with convolutional neural networkcited by 18
  4. 2020Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networkscited by 6
  5. 2020Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoisingcited by 2

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