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

Hammernik K, Kustner T, Yaman B, Huang Z, Rueckert D, Knoll F, Akcakaya M (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Signal Processing Magazine Institute of Electrical and Electronic Engineers

Book Volume: 40

Pages Range: 98-114

Journal Issue: 1

DOI: 10.1109/MSP.2022.3215288

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.

Authors with CRIS profile

Florian Knoll Professur für Computational Imaging

Involved external institutions

University of Minnesota (UMN) US United States (USA) (US) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB) New York University (NYU) US United States (USA) (US)

How to cite

APA:

Hammernik, K., Kustner, T., Yaman, B., Huang, Z., Rueckert, D., Knoll, F., & Akcakaya, M. (2023). Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging. IEEE Signal Processing Magazine, 40(1), 98-114. https://doi.org/10.1109/MSP.2022.3215288

MLA:

Hammernik, Kerstin, et al. "Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging." IEEE Signal Processing Magazine 40.1 (2023): 98-114.

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