Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence-Reference-Cited by-同舟云学术

Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence

Published:2023-03 Issue:2 Volume:40 Page:129-140
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Yang Qinqin¹^ORCID,Wang Zi²^ORCID,Guo Kunyuan¹,Cai Congbo¹^ORCID,Qu Xiaobo¹^ORCID

Affiliation:

1. Xiamen University, Xiamen, China

2. Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/79/10054188/10054205.pdf?arnumber=10054205

Reference30 articles.

1. In vivo imaging of phosphocreatine with artificial neural networks

2. Artificial neural network for multi‐echo gradient echo–based myelin water fraction estimation

3. Exponential Signal Reconstruction With Deep Hankel Matrix Factorization

4. Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

5. Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging

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