Adaptive High-frequency Enhancement Network with Equilibrated Mechanism for MR Imaging

Unrolling is a promising deep learning (DL)-based technique in accelerating Magnetic Resonance (MR) imaging. It incorporates deep learning networks into the iterative optimization algorithm, enabling the flexible and adaptive learning of undefined parameters or functions. However, unrolling methods employ a limited number of iterations owing to memory constraints and exhibit detail loss, which hinders their overall performance and practical applications. In this work, we propose a novel network that adaptively improves the high-frequency feature representation and integrates a deep equilibrium model for fixed-point iteration to enhance the robustness of the reconstruction. Experimental results demonstrate superior performance of the proposed method in detail preservation, generalization across various test datasets, and robustness against noise interference and number of iterations.