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

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}
}