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  DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification

Glang, F., Deshmane, A., Prokudin, S., Martin, F., Herz, K., Lindig, T., et al. (2020). DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.

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 Creators:
Glang, F1, 2, Author              
Deshmane, A1, 2, Author              
Prokudin, S, Author
Martin, F1, 2, Author              
Herz, K1, 2, Author              
Lindig, T, Author              
Bender, B, Author              
Scheffler, K1, 2, Author              
Zaiss, M1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2020-08
 Publication Status: Published online
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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
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Start-/End Date: 2020-08-08 - 2020-08-14

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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 0509 Start / End Page: 216 Identifier: -