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Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey

Research Article

GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

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  • @INPROCEEDINGS{10.4108/eai.21-11-2024.2354627,
        author={Yunuo  Wang and Ningning  Yang and Jialin  Li},
        title={GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising },
        proceedings={Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey},
        publisher={EAI},
        proceedings_a={CONF-MLA},
        year={2025},
        month={3},
        keywords={low-dose computed tomography denoising gan machine learning deep learning architecture optimization restoration},
        doi={10.4108/eai.21-11-2024.2354627}
    }
    
  • Yunuo Wang
    Ningning Yang
    Jialin Li
    Year: 2025
    GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising
    CONF-MLA
    EAI
    DOI: 10.4108/eai.21-11-2024.2354627
Yunuo Wang1,*, Ningning Yang2, Jialin Li3
  • 1: Huazhong University of Science and Technology
  • 2: New York University Shanghai
  • 3: Shanghai Jiao Tong University
*Contact email: wyunuo@hust.edu.cn

Abstract

Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rapid advancements in GAN-based LDCT denoising techniques, examining the evolution from foundational architectures to state-of-the-art models incorporating advanced features such as anatomical priors, perceptual loss functions, and innovative regularization strategies. We critically analyze various GAN architectures, including conditional GANs (cGANs), CycleGANs, and Super-Resolution GANs (SRGANs), elucidating their unique strengths and limitations in the context of LDCT denoising. The evaluation provides both qualitative and quantitative results related to the improvements in performance in benchmark and clinical datasets with metrics such as PSNR, SSIM, and LPIPS. After highlighting the positive results, we discuss some of the challenges preventing a wider clinical use, including the interpretability of the images generated by GANs,synthetic artifacts,and the need for clinically relevant metrics. The review concludes by highlighting the essential significance of GAN-based methodologies in the progression of precision medicine via tailored LDCT denoising models,underlining the transformative possibilities presented by artificial intelligence within contemporary radiological practice.

Keywords
low-dose computed tomography denoising gan machine learning deep learning architecture optimization restoration
Published
2025-03-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.21-11-2024.2354627
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