LCTU-Net: A U-Net with Lipschitz continuous transformer for low-dose CT denoising

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.

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Published
2024-11-08
DOI
https://doi.org/10.1117/12.3049484
Primary Topic
Medical Imaging Techniques and Applications
Type
article
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article

LCTU-Net: A U-Net with Lipschitz continuous transformer for low-dose CT denoising

Yakang Li, Weizhen Guo, Huaqiang Yuan
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Medical Imaging Techniques and Applications
article

LCTU-Net: A U-Net with Lipschitz continuous transformer for low-dose CT denoising

Yakang Li, Weizhen Guo, Huaqiang Yuan
article en

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.

Chinese Academy of Sciences (CN), Dongguan University of Technology (CN), Institute of High Energy Physics (CN)
Affordable and clean energy
Openalex Percentile: Top 67%
Medical Imaging Techniques and Applications
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