Perturbation Robust: Deep Adversarial-Equilibrium Unfolding Network for Magnetic Resonance Image Reconstruction

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.