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Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation

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Abstract

Magnetic resonance imaging (MRI) has become a widely used medical imaging method. Affected by imaging mechanism, magnetic field inhomogeneity and other factors, MRI images are often interfered by non-Gaussian noise such as Rician noise and non-central chi-square distribution noise. However, the existing MRI denoising methods cannot effectively remove different kinds of noise, and the image is prone to blur and detail loss, even artifacts. Thus, this paper proposes an adaptive denoising algorithm for MRI based on Nonlocal Structural Similarity and Low-Rank Sparse Representation (NSS-LRSR). Different from the existing methods, it is a new paradigm to adaptively filter non-Gaussian noise of MRI and it has a good effect on both spatially stable and spatially varying Rician or non-central chi-square distribution noise. The forward variance stable transformation is used to correct the deviation caused by non-Gaussian noise, and then the non-local information is regrouped. And considering the sparse of similar image blocks, we use improved weighted kernel norm minimization to represent the non-local image blocks based on estimation of noise’s standard deviation; thereby the processed image block are aggregated and outputted. Experimental results show that compared with the currently popular algorithms, the proposed NSS-LRSR achieves better results in PSNR and SSIM quantitative indexes.

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Data availability

The MRI data that support the findings of this study are available from the public BrainWeb dataset http://www.bic.mni.mcgill.ca/brainweb/.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 62001380).

Funding

Funding was provided by National Natural Science Foundation of China (No. 62001380).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Hongyu Wang, Songtao Ding & Xiaoying Pan

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, 710121, China

    Hongyu Wang, Songtao Ding & Xiaoying Pan

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an, 710121, China

    Hongyu Wang, Songtao Ding & Xiaoying Pan

  4. Department of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Ying Li & Jun Feng

  5. Cangzhou Hospital of Integrated TCM-WM·Hebei, Cangzhou, China

    Zhanyi Gao

  6. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110, China

    Shaohua Wan

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  1. Hongyu Wang
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Contributions

Conceptualization: HW; Methodology: YL; Formal analysis and investigation: XP; Writing—original draft preparation: YL; Writing—review and editing: HW, SD; Funding acquisition: HW; Resources: SW, JF; Supervision: SW, JF.

Corresponding authors

Correspondence to Shaohua Wan or Jun Feng.

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No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. The work described is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors have approved the manuscript that is enclosed.

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Wang, H., Li, Y., Ding, S. et al. Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation. Cluster Comput 26, 2933–2946 (2023). https://doi.org/10.1007/s10586-022-03773-2

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