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Multi-layer enhancement of low-dose CT images via adaptive fusion

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Abstract

In order to solve the problems of low SNR and low use value of low-dose CT images, this study proposes a multi- layer enhancement of low-dose CT images via adaptive fusion. In our study, the image denoising training based on generative adversarial network is carried out, and the perceptual loss and structural loss optimization generator are used to strengthen the denoising ability and retain the details of the image. To clearly observe the pathological tissue structure, it is necessary to perform a certain degree of image enhancement and image fusion on CT images. Using the real clinical data disclosed in the AAPM competition as the experimental dataset, in the image denoising experiment, the PSNR, SSIM, and RMSE are 33.0155, 0.9185, and 5.99. Compared to traditional methods, the effectiveness of the proposed method was better by 10.76%, 4.08% and 24.54% on average, respectively. The proposed model in this study obviously reduces the noise of CT images, and the obtained CT images are more detailed, its brightness and contrast are significantly enhanced, which proves the feasibility and effectiveness of the algorithm.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2020D01A131), the Teaching and Research Fund of Yangtze University (JY2020101), the Undergraduate Training Programs for Innovation and Entrepreneurship of Yangtze University under Grant Yz2020057, Yz2020059, Yz2020156, and the National College Student Innovation and Entrepreneurship Training Program (202110489003). In addition, we are particularly grateful to those who have allowed their imaging data to be shared for research purposes. We would also like to thank the work of many researchers in the collection, standardization, evaluation and annotation of the patient cases.

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Author notes

  1. Mei-Ran Li and Kai Xie have contributed equally to this work.

Authors and Affiliations

  1. School of Electronic Information, Yangtze University, Jingzhou, 434023, China

    Mei-Ran Li, Kai Xie & Hua-Quan Chen

  2. National Demonstration Center for Experimental Electrical and Electronic Education, Yangtze University, Jingzhou, 434023, China

    Mei-Ran Li, Kai Xie & Hua-Quan Chen

  3. Western Institute of Yangtze University, Karamay, 834000, China

    Mei-Ran Li, Kai Xie, Hua-Quan Chen & Chang Wen

  4. School of Computer Science, Yangtze University, Jingzhou, 434023, China

    Chang Wen

  5. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Jian-Biao He

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  1. Mei-Ran Li
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Correspondence to Kai Xie.

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Li, MR., Xie, K., Chen, HQ. et al. Multi-layer enhancement of low-dose CT images via adaptive fusion. SIViP 17, 1285–1295 (2023). https://doi.org/10.1007/s11760-022-02336-7

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  • DOI: https://doi.org/10.1007/s11760-022-02336-7

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