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Multi-Scale Feature Fusion Network for Low-Dose CT Denoising

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Abstract

Computed tomography (CT) is an imaging technique extensively used in medical treatment, but too much radiation dose in a CT scan will cause harm to the human body. Decreasing the dose of radiation will result in increased noise and artifacts in the reconstructed image, blurring the internal tissue and edge details. To get high-quality CT images, we present a multi-scale feature fusion network (MSFLNet) for low-dose CT (LDCT) denoising. In our MSFLNet, we combined multiple feature extraction modules, effective noise reduction modules, and fusion modules constructed using the attention mechanism to construct a horizontally connected multi-scale structure as the overall architecture of the network, which is used to construct different levels of feature maps at all scales. We innovatively define a composite loss function composed of pixel-level loss based on MS-SSIM-L1 and edge-based edge loss for LDCT denoising. In short, our approach learns a rich set of features that combine contextual information from multiple scales while maintaining the spatial details of denoised CT images. Our laboratory results indicate that compared with the existing methods, the peak signal-to-noise ratio (PSNR) value of CT images of the AAPM dataset processed by the new model is 33.6490, and the structural similarity (SSIM) value is 0.9174, which also achieves good results on the Piglet dataset with different doses. The results also show that the method removes noise and artifacts while effectively preserving CT images’ architecture and grain information.

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Data Availability

Data underlying the results presented in this paper are available in the AAPM Dataset, Ref [38] and Piglet Dataset, Ref [39].

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Funding

We would like to thank the editors and reviewers for their reviews that improved the content of this paper. This work was funded by the Science and Technology Innovation Project of Colleges and Universities of Shanxi Province (2020L0282); the Open Fund Project of Key Laboratory of Computer Network and Information Integration, Ministry of Education(K93-9–2022-02); the Postgraduate Education Innovation Project of Shanxi Province (2022Y582).

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Authors and Affiliations

  1. School of Information and Communication Engineering, North University of China, No.3, College Road, 030051, Taiyuan, Shanxi Province, China

    Zhiyuan Li, Yi Liu, Jing Lu, Jiaqi Kang & Zhiguo Gui

  2. State Key Laboratory of Dynamic Testing Technology, North University of China, 030051, Taiyuan, China

    Zhiyuan Li, Yi Liu, Jing Lu, Jiaqi Kang & Zhiguo Gui

  3. School of Computer Science and Engineering, Southeast University, 211189, Nanjing, Jiangsu, China

    Yang Chen

  4. Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, 211189, Nanjing, Jiangsu, China

    Huazhong Shu

  5. Key Laboratory of Computer Network and Information Integration Ministry of Education, Southeast University, 211189, Nanjing, Jiangsu, China

    Yang Chen

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Zhiyuan Li and Yi Liu. The first draft of the manuscript was written by Zhiyuan Li and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Li, Z., Liu, Y., Shu, H. et al. Multi-Scale Feature Fusion Network for Low-Dose CT Denoising. J Digit Imaging 36, 1808–1825 (2023). https://doi.org/10.1007/s10278-023-00805-0

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