Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
- DOI
- 10.1109/msp.2022.3215288
- Published
- 2023-01
- Container
- IEEE Signal Processing Magazine
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/msp.2022.3215288,
title = {Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging},
author = {Kerstin Hammernik and Thomas Kustner and Burhaneddin Yaman and Zhengnan Huang and Daniel Rueckert and Florian Knoll and Mehmet Akcakaya},
year = {2023},
journal = {IEEE Signal Processing Magazine},
doi = {10.1109/msp.2022.3215288},
url = {https://doi.org/10.1109/msp.2022.3215288}
}RIS
TY - JOUR TI - Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging AU - Kerstin Hammernik AU - Thomas Kustner AU - Burhaneddin Yaman AU - Zhengnan Huang AU - Daniel Rueckert AU - Florian Knoll AU - Mehmet Akcakaya PY - 2023 JO - IEEE Signal Processing Magazine DO - 10.1109/msp.2022.3215288 UR - https://doi.org/10.1109/msp.2022.3215288 ER -
APA
Hammernik, K., Kustner, T., Yaman, B., Huang, Z., Rueckert, D., Knoll, F., & Akcakaya, M. (2023). Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2022.3215288
Source records
- crossref · retrieved 2026-11-10T13:19:57.103Z