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  deepCEST: 9.4 T spectral super resolution from 3 T CEST MRI data: optimization of network architectures

Zaiss, M., Martin, F., Glang, F., Herz, K., Deshmane, A., Bender, B., et al. (2019). deepCEST: 9.4 T spectral super resolution from 3 T CEST MRI data: optimization of network architectures. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-96FF-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-B710-B
Genre: Poster

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 Creators:
Zaiss, M1, 2, Author              
Martin, F1, 2, Author              
Glang, F1, 2, Author              
Herz, K1, 2, Author              
Deshmane, A1, 2, Author              
Bender, B, Author              
Lindig, T, Author              
Scheffler, K1, 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: Different neural network architectures for predicting 9T CEST contrasts from 3T spectral data are investigated as well as the influence of different training data sets on the quality of resulting predictions. Although optimized convolutional neural network (CNN) architectures perform well, the best results were reached with a simpler feedforward neural network (FFNN). As CNNs have many hyperparameters to tune, this work forms a basis for CNN architecture optimization for the proposed super-resolution CEST application.

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 Dates: 2019-05
 Publication Status: Published online
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Title: 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019)
Place of Event: Montréal, QC, Canada
Start-/End Date: 2019-05-11 - 2019-05-16

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Title: ISMRM 27th Annual Meeting & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 4016 Start / End Page: - Identifier: -