@misc{item_3248020,
  title = {{DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification}},
  booktitle = {{2020 ISMRM \& SMRT Virtual Conference \& Exhibition}},
  abstract = {{Analysis of CEST data often requires complex mathematical modeling before contrast generation, which can be error prone and time-consuming. Here, a probabilistic deep learning approach is introduced to shortcut conventional Lorentzian fitting analysis of 3T in-vivo CEST data by learning from previously evaluated data. It is demonstrated that the trained networks generalize to data of a healthy subject and a brain tumor patient, providing CEST contrasts in a fraction of the conventional evaluation time. Additionally, the probabilistic network architecture enables uncertainty quantification, indicating if predictions are trustworthy, which is assessed by perturbation analysis.}},
  pages = {216},
  year = {2020},
  author = {Glang, F and Deshmane, A and Prokudin, S and Martin, F and Herz, K and Lindig, T and Bender, B and Scheffler, K and Zaiss, M}
}