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SwinCT: feature enhancement based low-dose CT images denoising with swin transformer

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Abstract

To reduce the potential harm to patients from X-ray radiation in computed tomography (CT), low-dose ray CT (LDCT) was conspicuous in clinical diagnosis and evaluation. However, the excessive noises in the LDCT scan significantly degrades the image quality, which seriously affects the clinical diagnostic efficacy. In this paper, we propose SwinCT, a feature-enhanced model for LDCT images noise reduction. SwinCT employs the feature enhancement module (FEM) based on Swin Transformer to extract and augment the high-level features of medical images, and simultaneously combines with the deep noise reduction encoder-decoder network in the downstream task, thus ensuring that more tissue and lesion details are retained after images denoising. Compared with the original LDCT images of noisy surrounding, the denoised image quality is significantly improved by the devised SwinCT denoising model, and the performance metrics of our method are also competitive with other advanced LDCT image denoising methods.

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Data availability

The data and references presented in this study are available from the corresponding author upon reasonable request.

Notes

  1. https://pydicom.github.io/.

  2. https://pytorch.org/.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (61976123, 62072213); Taishan Young Scholars Program of Shandong Province; and Key Development Program for Basic Research of Shandong Province (ZR2020ZD44).

Author information

Author notes

  1. Muwei Jian and Xiaoyang Yu contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Muwei Jian, Xiaoyang Yu & Haoran Zhang

  2. School of Information Science and Technology, Linyi University, Linyi, China

    Muwei Jian & Chengdong Yang

Authors
  1. Muwei Jian
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  2. Xiaoyang Yu
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  3. Haoran Zhang
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  4. Chengdong Yang
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All authors wrote the main manuscript text and prepared figures and tables. All authors reviewed the manuscript.

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Correspondence to Muwei Jian.

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Communicated by A. Liu.

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Jian, M., Yu, X., Zhang, H. et al. SwinCT: feature enhancement based low-dose CT images denoising with swin transformer. Multimedia Systems 30, 1 (2024). https://doi.org/10.1007/s00530-023-01202-x

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