Deep equilibrium–adversarial robust unfolding network for MRI reconstruction

DEAR-net enhances the robustness of the reconstruction process in the presence of mild noise and artifacts in under-sampled k-space. Furthermore, we provide a mathematical analysis of the reconstruction error.

Authors

Zhuo‐Xu Cui (ORCID: https://orcid.org/0000-0001-9283-881X)
Liang Dong (ORCID: https://orcid.org/0000-0002-3646-8452)
Congcong Liu (ORCID: https://orcid.org/0000-0003-3949-4194)
Kun Shang (ORCID: https://orcid.org/0000-0003-3826-1102)
Tian Zhou (ORCID: https://orcid.org/0009-0009-7370-3851)

Institutions

Chinese Academy of Sciences (CN)
HKUST Shenzhen Research Institute (CN)
Shenzhen Institutes of Advanced Technology (CN)
University of Chinese Academy of Sciences (CN)

Publication Details

Journal: Medical Physics
Published: 2025-12-01
DOI: https://doi.org/10.1002/mp.70185
Primary Topic: Sparse and Compressive Sensing Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Deep equilibrium–adversarial robust unfolding network for MRI reconstruction

Zhuo‐Xu Cui, Liang Dong, Congcong Liu, Kun Shang et al.

Dec 1, 2025

Medical Physics

Sparse and Compressive Sensing Techniques

article

Deep equilibrium–adversarial robust unfolding network for MRI reconstruction

Zhuo‐Xu Cui, Liang Dong, Congcong Liu, Kun Shang, Tian Zhou

December 1, 2025article en

Abstract

DEAR-net enhances the robustness of the reconstruction process in the presence of mild noise and artifacts in under-sampled k-space. Furthermore, we provide a mathematical analysis of the reconstruction error.

Medical PhysicsVol. 52(12)

Chinese Academy of Sciences (CN), HKUST Shenzhen Research Institute (CN), Shenzhen Institutes of Advanced Technology (CN), University of Chinese Academy of Sciences (CN)

Openalex Percentile: Top 59%

Sparse and Compressive Sensing Techniques

DOI Source OpenAlex

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