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  DeepCEST: 7T Chemical exchange saturation transfer MRI contrast inferred from 3T data via deep learning with uncertainty quantification

Hunger, L., German, A., Glang, F., Khakzar, K., Dang, N., Mennecke, A., et al. (2021). DeepCEST: 7T Chemical exchange saturation transfer MRI contrast inferred from 3T data via deep learning with uncertainty quantification. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).

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 Creators:
Hunger, LE, Author
German, A, Author
Glang, F1, 2, Author              
Khakzar, KM, Author
Dang, N, Author
Mennecke, A, Author
Maier, A, Author
Laun, F, 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: The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and predict the contrasts of a higher field strength. This is possible through the application of a neural network, which was trained with low and high B1 Z-spectra acquired at 3T as input data, and as target data 5-pool-Lorentzian fitted amplitudes obtained from 7T spectra were used. The network included an uncertainty quantification to verify the reliability of the predicted images.

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 Dates: 2021-05
 Publication Status: Published online
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Title: 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021)
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Start-/End Date: 2021-05-15 - 2021-05-20

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Title: 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 1451 Start / End Page: - Identifier: -