Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems–Relaxometry and field mapping from multi-echo data.


Conference paper


Benslimane I, Jochmann T, Zivadinov R, Schweser F
Proc Intl Soc Mag Reson Med, 2021, p. 330

Cite

Cite

APA   Click to copy
I, B., T, J., R, Z., & F, S. (2021). Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems–Relaxometry and field mapping from multi-echo data. (p. 330).


Chicago/Turabian   Click to copy
I, Benslimane, Jochmann T, Zivadinov R, and Schweser F. “Unsupervised Physics-Informed Deep Learning (N=1) for Solving Inverse QMRI Problems–Relaxometry and Field Mapping from Multi-Echo Data.” In , 330. Proc Intl Soc Mag Reson Med, 2021.


MLA   Click to copy
I, Benslimane, et al. Unsupervised Physics-Informed Deep Learning (N=1) for Solving Inverse QMRI Problems–Relaxometry and Field Mapping from Multi-Echo Data. 2021, p. 330.


BibTeX   Click to copy

@inproceedings{benslimane2021a,
  title = {Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems–Relaxometry and field mapping from multi-echo data.},
  year = {2021},
  pages = {330},
  series = {Proc Intl Soc Mag Reson Med},
  author = {I, Benslimane and T, Jochmann and R, Zivadinov and F, Schweser}
}





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