Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues

Knoll F, Hammernik K, Zhang C, Moeller S, Pock T, Sodickson DK, Akcakaya M (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Signal Processing Magazine Institute of Electrical and Electronic Engineers

Book Volume: 37

Pages Range: 128-140

Article Number: 8962951

Journal Issue: 1

DOI: 10.1109/MSP.2019.2950640

Abstract

Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deeplearning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for both low-dose computed tomography and accelerated MRI. The additional integration of multicoil information to recover missing k-space lines in the MRI reconstruction process is studied less frequently, even though it is the de facto standard for the currently used accelerated MR acquisitions. This article provides an overview of the recent machine-learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given and structured around the classical view of image- and k-space-based methods. Linear and nonlinear methods are covered, followed by a discussion of the recent efforts to further improve parallel imaging using machine learning and, specifically, artificial neural networks. Image domain-based techniques that introduce improved regularizers are covered as well as k-space-based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed and recent efforts for producing open data sets and benchmarks for the community are examined.

Authors with CRIS profile

Florian Knoll

Involved external institutions

New York University (NYU) US United States (USA) (US) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Institute of Electrical and Electronics Engineers (IEEE) US United States (USA) (US) University of Minnesota (UMN) US United States (USA) (US) Rheinische Friedrich-Wilhelms-Universität Bonn DE Germany (DE)

How to cite

APA:

Knoll, F., Hammernik, K., Zhang, C., Moeller, S., Pock, T., Sodickson, D.K., & Akcakaya, M. (2020). Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. IEEE Signal Processing Magazine, 37(1), 128-140. https://dx.doi.org/10.1109/MSP.2019.2950640

MLA:

Knoll, Florian, et al. "Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues." IEEE Signal Processing Magazine 37.1 (2020): 128-140.

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