No-reference denoising of low-dose CT projections
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Abstract (arXiv)
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised approaches, the proposed method requires only noisy CT projections and exploits the connections between adjacent images. The experiments carried out on an LDCT dataset demonstrate that our method is almost as accurate as the supervised approach, while also outperforming the considered self-supervised denoising methods.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 3 Feb 2021.
Builds on
10 of 18 references.
References · 10
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
- 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
- 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
- 2019Robust and interpretable blind image denoising via bias-free convolutional neural networkscited by 87
- 20183D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Networkcited by 47
- 2018Deep Sequential Segmentation of Organs in Volumetric Medical Scanscited by 6
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Citing papers per year: 2022: 1.
1 paper.