Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

• URL: http://arxiv.org/abs/2203.12215v1
• Date: Wed, 23 Mar 2022 06:04:11 GMT
• Title: Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
• Authors: Kerstin Hammernik, Thomas K\"ustner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Ak\c{c}akaya
• Abstract summary: Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction.
• Score: 7.404599356384102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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