Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging-Reference-Cited by-同舟云学术

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

Published:2023-01 Issue:1 Volume:40 Page:98-114
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Hammernik Kerstin¹^ORCID,Kustner Thomas²^ORCID,Yaman Burhaneddin³^ORCID,Huang Zhengnan⁴,Rueckert Daniel¹^ORCID,Knoll Florian⁵^ORCID,Akcakaya Mehmet⁶^ORCID

Affiliation:

1. Department of Computing, Imperial College London, London, U.K.

2. School of Biomedical Engineering and Imaging Sciences, King’s College London, U.K.

3. Electrical Engineering, University of Minnesota, Minneapolis, MN, USA

4. Biomedical Imaging, New York University School of Medicine, New York, NY, USA

5. Department of Artificial Intelligence in Biomedical Engineering, Friedrich–Alexander University of Erlangen–Nuremberg, Erlangen, Germany

6. Associate Professor, University of Minnesota, Minneapolis, MN, USA

Funder

National Institutes of Health

National Science Foundation

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/79/10004746/10004819-aam.pdf

Reference40 articles.

1. Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging

2. LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging

3. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

4. Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction

5. RARE: Image Reconstruction Using Deep Priors Learned Without Groundtruth

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