Keywords: AI/ML Image Reconstruction, Data Processing
Motivation: Deep unfolding neural networks had attained great success in solving tasks of magnetic resonance image (MRI) reconstruction.
Goal(s): However, minor perturbation in MR signals can result in significant distortions such as some artifacts of the reconstructed images via previous deep unfolding methods.
Approach: This paper proposes a deep equilibrium unfolding network based on adversarial learning to improve robustness of unfolding networks.
Results: Experiment results demonstrate that the proposed method obtains better reconstructed MR images compared with baseline-networks when some artifacts exist in under-sampled multi-channel k-space data.
Impact: We propose a robust method for MRI reconstruction against artefacts in k-space data.
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