ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising

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...

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Veröffentlicht in:Biomedical optics express Jg. 13; H. 11; S. 5775 - 5793
Hauptverfasser: Liu, Han, Liao, Peixi, Chen, Hu, Zhang, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Optica Publishing Group 01.11.2022
ISSN:2156-7085, 2156-7085
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2156-7085
2156-7085
DOI:10.1364/BOE.471340