Magnetic resonance imaging (MRI) is a principal radiological modality that
provides radiation-free, abundant, and diverse information about the whole
human body for medical diagnosis, but suffers from prolonged scan time. The
scan time can be significantly reduced through k-space undersampling but the
introduced artifacts need to be removed in image reconstruction. Although deep
learning (DL) has emerged as a powerful tool for image reconstruction in fast
MRI, its potential in multiple imaging scenarios remains largely untapped. This
is because not only collecting large-scale and diverse realistic training data
is generally costly and privacy-restricted, but also existing DL methods are
hard to handle the practically inevitable mismatch between training and target
data. Here, 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. For a 2D
image, the reconstruction is separated into many 1D basic problems and starts
with the 1D data synthesis, to facilitate generalization. We demonstrate that
training DL models on synthetic data, integrated with enhanced learning
techniques, can achieve comparable or even better in vivo MRI reconstruction
compared to models trained on a matched realistic dataset, reducing the demand
for real-world MRI data by up to 96%. Moreover, our PISF shows impressive
generalizability in multi-vendor multi-center imaging. Its excellent
adaptability to patients has been verified through 10 experienced doctors'
evaluations. PISF 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.Comment: 22 pages, 9 figures, 1 tabl