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Link to original content: https://doi.org/10.1007/s11760-024-03280-4
Fourth-order partial differential diffusion model with adaptive Laplacian kernel for low-dose CT image processing | Signal, Image and Video Processing Skip to main content
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Fourth-order partial differential diffusion model with adaptive Laplacian kernel for low-dose CT image processing

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Abstract

Low-dose computed tomography (LDCT) reduces radiation damage to patients, however, it adds noise and artifacts to the reconstructed images, which deteriorates the quality of CT images. To address the problem of easy edge blurring in the denoising process of LDCT images, this paper proposes a fourth-order partial differential diffusion model with adaptive Laplacian kernel that can protect edge and detail information. The model incorporates the guided filter with edge preserving as a fidelity term in the energy function. Then, using gradient magnitude and the local variance to construct edge and detail detectors in the diffusion function, which can protect the edge and detail information of the LDCT images during the diffusion process. Finally, using the adaptive Laplacian kernel replaces the conventional Laplacian kernel, which has stronger edge preserving. The experimental results show that the proposed model achieves excellent performance in edge preserving and noise suppression in actual thoracic phantom and AAPM clinical LDCT images. In terms of both visual effects and quantitative indexes, the proposed model has good processing performance compared with other excellent algorithms.

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Data availability

The source code and the images used in this paper have been uploaded to GitHub at https://github.com/LeiWang-learning/ALFOD.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grant (61801438), the Science and Technology Innovation Project of Colleges and Universities of Shanxi Province under Grant (2020L0282), the Natural Science Foundation of Shanxi Province under Grant (202103021224204).

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Authors and Affiliations

  1. State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, 030051, China

    Lei Wang, Yi Liu, Rongbiao Yan, Shilei Ren, Yan Chen & Zhiguo Gui

  2. Shanxi North Xing’an Chemical Industry Co.Ltd, Taiyuan, 030008, China

    Rui Wu

  3. Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China

    Yuhang Liu

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  1. Lei Wang
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Contributions

Lei Wang, Yi Liu, and Zhiguo Gui wrote the main manuscript text, Rui Wu, RongbiaoYan, Yuhang Liu prepared figures and tables. Shilei Ren and Yan Chen participated in the discussion and provided some advice. All authors reviewed the manuscript.

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Correspondence to Zhiguo Gui.

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Wang, L., Liu, Y., Wu, R. et al. Fourth-order partial differential diffusion model with adaptive Laplacian kernel for low-dose CT image processing. SIViP 18, 5907–5917 (2024). https://doi.org/10.1007/s11760-024-03280-4

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