Dual‐branch guided multi‐scale half‐instance normalization network for low‐dose CT image denoising

The proposed DGMINet model achieves significant improvements in LDCT image denoising, offering an effective solution that removes noise while preserving crucial image details. Its outstanding performance and relatively efficient inference time highlight the model's potential for real-world clinical applications.

Authors

Shun Wei (ORCID: https://orcid.org/0009-0003-8057-6834)
Ce Ge (ORCID: https://orcid.org/0000-0003-4312-0152)
Yan Cui (ORCID: https://orcid.org/0009-0003-5909-3299)
Jielin Jiang (ORCID: https://orcid.org/0009-0006-2447-1968)

Institutions

Nanjing Tech University (CN)
Nanjing Normal University (CN)
Nanjing University of Information Science and Technology (CN)
Jiangsu Industry Technology Research Institute (CN)
Nanjing University (CN)

Publication Details

Journal: Medical Physics
Published: 2025-10-01
DOI: https://doi.org/10.1002/mp.70046
Primary Topic: Medical Imaging Techniques and Applications
Type: article
Field-Weighted Citation Impact: 0.00

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article

Dual‐branch guided multi‐scale half‐instance normalization network for low‐dose CT image denoising

Shun Wei, Ce Ge, Yan Cui, Jielin Jiang

Oct 1, 2025

Medical Physics

Medical Imaging Techniques and Applications

article

Dual‐branch guided multi‐scale half‐instance normalization network for low‐dose CT image denoising

Shun Wei, Ce Ge, Yan Cui, Jielin Jiang

October 1, 2025article en

Abstract

The proposed DGMINet model achieves significant improvements in LDCT image denoising, offering an effective solution that removes noise while preserving crucial image details. Its outstanding performance and relatively efficient inference time highlight the model's potential for real-world clinical applications.

Medical PhysicsVol. 52(10)

Nanjing Tech University (CN), Nanjing Normal University (CN), Nanjing University of Information Science and Technology (CN), Jiangsu Industry Technology Research Institute (CN), Nanjing University (CN)

Openalex Percentile: Top 58%

Medical Imaging Techniques and Applications

DOI Source OpenAlex

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