  Physics-driven deep learning methods have emerged as a powerful tool for
computational magnetic resonance imaging (MRI) problems, pushing reconstruction
performance to new limits. This article provides an overview of the recent
developments in incorporating physics information into learning-based MRI
reconstruction. We consider inverse problems with both linear and non-linear
forward models for computational MRI, and review the classical approaches for
solving these. We then focus on physics-driven deep learning approaches,
covering physics-driven loss functions, plug-and-play methods, generative
models, and unrolled networks. We highlight domain-specific challenges such as
real- and complex-valued building blocks of neural networks, and translational
applications in MRI with linear and non-linear forward models. Finally, we
discuss common issues and open challenges, and draw connections to the
importance of physics-driven learning when combined with other downstream tasks
in the medical imaging pipeline.
