  Deep learning has innovated the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews
an emerging paradigm, imaging physics-based data synthesis (IPADS), that can
provide huge training data in biomedical magnetic resonance without or with few
real data. Following the physical law of magnetic resonance, IPADS generates
signals from differential equations or analytical solution models, making the
learning more scalable, explainable, and better protecting privacy. Key
components of IPADS learning, including signal generation models, basic deep
learning network structures, enhanced data generation, and learning methods are
discussed. Great potentials of IPADS have been demonstrated by representative
applications in fast imaging, ultrafast signal reconstruction and accurate
parameter quantification. Finally, open questions and future work have been
discussed.
