Paper
23 May 2023 Unsupervised low-dose CT image denoising using CycleGAN with selective spatial pyramid attention
Wanxin Sun, Jinke Zhang, Dejing Hao, Yingying Lin, Yuanke Zhang
Author Affiliations +
Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451K (2023) https://doi.org/10.1117/12.2681105
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
Low-Dose CT (LDCT) imaging technique can reduce the radiation exposure to patients but would inevitably introduce serious noise and artifacts to the CT images. Deep learning based LDCT denoising methods with supervised learning strategy have shown promising performance. However, they heavily rely on well-aligned training image pairs that are in fact very difficult to be obtained in clinical practice. In this study, we propose a CycleGAN-based unsupervised LDCT image denoising method with a novel Selective Spatial Pyramid Attention block (SSPA). The proposed SSPA can effectively fuse the attention map with the original feature map through an attention scheme, thus better extract the representative features for fine structure restoration. Experimental results show that the proposed unsupervised method achieves state-of-the-art denoising performance for LDCT images.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wanxin Sun, Jinke Zhang, Dejing Hao, Yingying Lin, and Yuanke Zhang "Unsupervised low-dose CT image denoising using CycleGAN with selective spatial pyramid attention", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451K (23 May 2023); https://doi.org/10.1117/12.2681105
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KEYWORDS
Denoising

Image denoising

Computed tomography

Machine learning

Education and training

Feature extraction

Dysprosium

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