Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

By Dong Liang, Jing Cheng, Ziwen Ke, Leslie Ying. IEEE Signal Processing Magazine, 2020

346 citations recorded by the source corpus.

Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deep-learning-based image reconstruction methods for MRI. Two types of deep-learningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1e8a5d2c359ead7ab2e55e1d492a7ed1e2808d0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Zhangyang Wang
2. Wotao Yin
3. Gitta Kutyniok

In Radiology, Nuclear Medicine and Imaging

1. Zhangyang Wang
2. Wotao Yin
3. Yin Zhang

Citations in the corpus

• Learning to Optimize: A Primer and A Benchmark (2021, methodology): “Application areas benefiting from L2O methods include computer vision [223; 255; 58], medical imaging [124; 249], signal processing and communication [31; 15], policy learning [143], game theory [209; 210; 85], computational biology [37; 46], and even software engineering [8].”