Motivation: Deep learning (DL) is powerful for fast MRI reconstruction, but remains largely untapped in multiple clinical imaging scenarios. Goal(s): To provide a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications. Approach: In this work, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. Results: PISF trained on synthetic data enables high-quality, ultra-fast, and robust MRI reconstruction from different 4contrasts, 5 anatomies, 5 vendors and centers, and 2 pathologies, without further re-training. Impact: Physics-informed synthetic data learning (DL) provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.

Physics-informed Synthetic Data Learning Boosts Multi-Scenario Fast MRI Reconstruction