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Low-dose CT image denoising based on edge prior and high-frequency sensitive feature fusion network

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Abstract

Low-dose CT (LDCT) is a feasible method to reduce the radiation dose to the patient. However, both artifacts and noise appear in the reconstructed images, reducing the clarity of the images. To remove artifacts and noise and improve image quality, a deep learning method based on edge prior and high-frequency sensitive fusion network (EHSFN) is proposed. The EHSFN first decomposes the LDCT image into high-frequency (HF) and low-frequency (LF) parts for decoupling the artifact noise from the LF part. And then a trainable edge prior is introduced to enrich tissue edge information and guide the extraction of tissue structure feature in HF part. To fully extract and fuse edge and HF information, a multi-stage feature extraction module and a multi-level fusion module are designed. Besides, edge loss, HF loss and reconstruction loss are applied to guide the training of the network. Experimental results show that the proposed network can not only effectively remove noise and artifacts but also retain more texture and tissue details, compared with the existing network.

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Availability of data and materials

We used a clinical dataset, which has been authorized by Mayo Clinic for “The 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge”. [Online], http://www.aapm.org/GrandChallenge/LowDoseCT.

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Acknowledgements

This work was supported in part by the national natural science foundation of China (No. 62001321), in part by the natural science foundation of Shanxi province (No. 201901D111261, No. 20210302 1224274, No.202103021224265), in part by research project supported by Shanxi scholarship council of China (2022-163) and the Taiyuan University of Science and Technology doctoral promoter (20212033).

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Authors and Affiliations

  1. School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Xueying Cui, Yingting Guo, Wenqiang Hao, Bin Liu & Lizhong Jin

  2. School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Hong Shangguan, Xiong Zhang & Anhong Wang

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  1. Xueying Cui
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  2. Yingting Guo
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  3. Wenqiang Hao
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  4. Hong Shangguan
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  5. Xiong Zhang
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  6. Bin Liu
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  7. Anhong Wang
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  8. Lizhong Jin
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Contributions

XC conceived the study design and wrote the manuscript, YG performed the experiments and the data analysis, WH debugged the code, all authors read and approved the final manuscript.

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Correspondence to Xueying Cui.

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Cui, X., Guo, Y., Hao, W. et al. Low-dose CT image denoising based on edge prior and high-frequency sensitive feature fusion network. SIViP 17, 3387–3396 (2023). https://doi.org/10.1007/s11760-023-02560-9

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  • DOI: https://doi.org/10.1007/s11760-023-02560-9

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