Uncertainty-weighted dual-domain consistency for self-supervised accelerated MRI reconstruction
Accelerated magnetic resonance imaging (MRI) typically relies on fully sampled k-space data to train supervised reconstruction networks, which are difficult to acquire in clinical practice. To eliminate this dependency, we propose a novel dual-domain self-supervised learning (Dual-SSL) framework that rigorously enforce...