Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

Kerstin Hammernik, Thomas Kustner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Akcakaya

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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 nonlinear 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 (PnP) 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 nonlinear forward models. Finally, we discuss common issues and open challenges, and we draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.

Original languageEnglish (US)
Pages (from-to)98-114
Number of pages17
JournalIEEE Signal Processing Magazine
Volume40
Issue number1
DOIs
StatePublished - Jan 1 2023

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