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.
@article{arxiv.2203.12215,
title = {Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging},
author = {Kerstin Hammernik and Thomas Küstner and Burhaneddin Yaman and Zhengnan Huang and Daniel Rueckert and Florian Knoll and Mehmet Akçakaya},
journal= {arXiv preprint arXiv:2203.12215},
year = {2022}
}