  Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet
its extended scanning times often compromise patient comfort and image quality,
especially in volumetric, temporal and quantitative scans. This review
elucidates recent advances in MRI acceleration via data and physics-driven
models, leveraging techniques from algorithm unrolling models,
enhancement-based models, and plug-and-play models to emergent full spectrum of
generative models. We also explore the synergistic integration of data models
with physics-based insights, encompassing the advancements in multi-coil
hardware accelerations like parallel imaging and simultaneous multi-slice
imaging, and the optimization of sampling patterns. We then focus on
domain-specific challenges and opportunities, including image redundancy
exploitation, image integrity, evaluation metrics, data heterogeneity, and
model generalization. This work also discusses potential solutions and future
research directions, emphasizing the role of data harmonization, and federated
learning for further improving the general applicability and performance of
these methods in MRI reconstruction.
