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8 November 2024 LCTU-Net: A U-Net with Lipschitz continuous transformer for low-dose CT denoising
Weizhen Guo, Huaqiang Yuan, Yakang Li
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Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341632 (2024) https://doi.org/10.1117/12.3049484
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
Due to the constraints of reduced radiation doses, low-dose computed tomography (LDCT) images frequently suffer from increased noise levels. To address this challenge, we developed the LCTU-Net, a network that incorporates a Lipschitz continuous transformer to enhance the capability of feature extraction. This new approach replaces traditional Transformer components, improving the efficiency of loss reduction and achieving lower loss levels. The U-Net architecture integrated within LCTU-Net plays a crucial role in effectively reducing noise interference in the images. Experimental results have demonstrated that LCTU-Net significantly outperforms existing denoising technologies, particularly in its ability to preserve intricate image details while effectively reducing noise.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Weizhen Guo, Huaqiang Yuan, and Yakang Li "LCTU-Net: A U-Net with Lipschitz continuous transformer for low-dose CT denoising", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341632 (8 November 2024); https://doi.org/10.1117/12.3049484
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KEYWORDS
Transformers
Denoising
Convolution
Education and training
Image processing
Feature extraction
Image quality
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