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

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Zaiss,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Martin,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Glang,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Herz,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Deshmane,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0003-96FF-5
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.