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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

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Deep equilibrium-adversarial robust unfolding network for MRI reconstruction.

Tian Zhou^1,2, Kun Shang^1,3, Congcong Liu⁴

¹Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.

Medical Physics

|December 13, 2025

Summary

A new deep equilibrium-adversarial robust unfolding network (DEAR-net) improves magnetic resonance imaging (MRI) reconstruction by enhancing robustness against k-space artifacts and noise, leading to higher image quality.

Keywords:

mathematical reconstruction error analysis robust MRI reconstruction stable adversarial learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Deep unfolding neural networks offer efficient MRI reconstruction but are vulnerable to artifacts and noise.
Iterative optimization in these networks can amplify signal perturbations, degrading image quality.

Purpose of the Study:

To develop a robust framework for MRI reconstruction that mitigates artifacts and noise in k-space.
To enhance the stability of the MRI reconstruction process.

Main Methods:

Proposed a novel deep equilibrium-adversarial robust unfolding network (DEAR-net).
Integrated adversarial learning with deep equilibrium architectures to suppress perturbations.
Utilized deep equilibrium architectures to maintain reconstruction stability.

Main Results:

DEAR-net demonstrated superior reconstruction performance.
Achieved higher image quality and enhanced robustness against k-space noise and artifacts.
Validated on fastMRI knee and private brain datasets.

Conclusions:

DEAR-net improves MRI reconstruction robustness against mild k-space noise and artifacts.
The framework enhances image quality in under-sampled k-space data.
Mathematical analysis of reconstruction error was provided.