  Deep learning (DL) has recently emerged as a pivotal technology for enhancing
magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This
review paper provides a comprehensive overview of recent advances in DL for MRI
reconstruction. It focuses on DL approaches and architectures designed to
improve image quality, accelerate scans, and address data-related challenges.
These include end-to-end neural networks, pre-trained networks, generative
models, and self-supervised methods. The paper also discusses the role of DL in
optimizing acquisition protocols, enhancing robustness against distribution
shifts, and tackling subtle bias. Drawing on the extensive literature and
practical insights, it outlines current successes, limitations, and future
directions for leveraging DL in MRI reconstruction, while emphasizing the
potential of DL to significantly impact clinical imaging practices.
