ArticleBiomedical optics express2022
ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- SAFFusion: a saliency-aware frequency fusion network for multimodal medical image fusion.Biomedical optics express · 2025Article
- Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.Scientific reports · 2025Article
- A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.Biomedical engineering letters · 2024Review
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Authors and funding
4 authors.
Funding
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Abstract
Computed tomography (CT) has become a powerful tool for medical diagnosis. However, minimizing X-ray radiation risk for the patient poses significant challenges to obtain suitable low dose CT images. Although various low-dose CT methods using deep learning techniques have produced impressive results, convolutional neural network based methods focus more on local information and hence are very limited for non-local information extraction. This paper proposes ERA-WGAT, a residual autoencoder incorporating an edge enhancement module that performs convolution with eight types of learnable operators providing rich edge information and a window-based graph attention convolutional network that combines static and dynamic attention modules to explore non-local self-similarity. We use the compound loss function that combines MSE loss and multi-scale perceptual loss to mitigate the over-smoothing problem. Compared with current low-dose CT denoising methods, ERA-WGAT confirmed superior noise suppression and perceived image quality.
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Registered trials
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