Low-dose CT image denoising network based on adaptive global context attention

Jinke Zhang; Wanxin Sun; YingYing Lin; Dejing Hao; Yuanke Zhang

doi:10.1117/12.2681016

23 May 2023 Low-dose CT image denoising network based on adaptive global context attention

Jinke Zhang, Wanxin Sun, YingYing Lin, Dejing Hao, Yuanke Zhang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451J (2023) https://doi.org/10.1117/12.2681016
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Low-Dose CT (LDCT) scanning can greatly reduce the radiation damage to patients but would introduce serious noise and artifacts to CT images. The traditional deep learning based LDCT denoising methods are fundamentally based on the convolution operations, while their receptive fields are small and cannot capture long-range correlations. The Global Context (GC) attention modeling mechanism can solve this problem with significantly less computational burden. However, the only one global context feature used in the GC block generally cannot well describe the non-homogenous structural statistic property of LDCT images. To this issue, this paper proposes an Adaptive Global Context (AGC) modeling scheme for better representing local contextual semantic information of CT images. An AGC-based residual auto encoder-decoder network (AGC-RED) is further proposed for efficient LDCT image noise reduction. The effectiveness of the presented AGC-RED network is validated by experimental clinical studies.

Citation Download Citation

Jinke Zhang, Wanxin Sun, YingYing Lin, Dejing Hao, and Yuanke Zhang "Low-dose CT image denoising network based on adaptive global context attention", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451J (23 May 2023); https://doi.org/10.1117/12.2681016

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