Coupled Spatial–Spectral Learning for Generative MRI Reconstruction with Enhanced Diagnostic Separability

Accelerated Magnetic Resonance Imaging (MRI) reconstruction is fundamentally an ill-posed inverse problem where aggressive undersampling of the frequency domain (k-space) leads to severe aliasing and the loss of high-frequency structural details. While deep generative adversarial networks (GANs) have established new benchmarks for this task, they frequently struggle with frequency-domain consistency, yielding reconstructions with over-smoothed textures and blurred semantic boundaries. To address this and improve diagnostic separability, we introduce AUSGAN, a novel generative framework that couples spatial attention with spectral regularization to enforce structural fidelity across dual domains. Specifically, the generator integrates soft attention mechanisms to dynamically gate high-frequency features along semantic edges, while the discriminator employs an azimuthal integration module to explicitly penalize distributional shifts in the polar spectral profile. This coupled spatial-spectral synergy prevents the hallucination of isotropic noise while strictly constraining high-frequency recovery to valid anatomical regions. We benchmark AUSGAN against state-of-the-art reconstruction baselines under highly aggressive undersampling rates (up to 10x acceleration) across three diverse clinical cohorts: the ISBR dataset and multi-modal datasets for Brain Metastasis and Liver Cirrhosis. Going beyond standard pixel-wise metrics, we introduce a boundary-aware evaluation framework leveraging the Bhattacharyya distance to quantitatively measure the feature separability of the reconstructed outputs. Extensive experiments demonstrate that AUSGAN establishes a new state-of-the-art, delivering superior perceptual quality, robust artifact suppression, and significantly enhanced semantic contrast at critical tissue interfaces.