Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks-Reference-Cited by-同舟云学术

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks

Published:2023-03 Issue:2 Volume:40 Page:116-128
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Zhu Yanjie¹,Cheng Jing²,Cui Zhuo-Xu³,Zhu Qingyong⁴,Ying Leslie⁵,Liang Dong¹

Affiliation:

1. Professor, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

2. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

3. Research Associate, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

4. Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

5. Clifford C. Furnas Chair Professor of Biomedical Engineering and Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/79/10054188/10054204.pdf?arnumber=10054204

Reference51 articles.

1. Single‐shot T 2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

2. Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks

3. qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

4. MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

5. Transform Learning for Magnetic Resonance Image Reconstruction: From Model-Based Learning to Building Neural Networks

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