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Abstract #4016

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

Moritz Zaiss1, Florian Martin1, Felix Glang1, Kai Herz1, Anagha Deshmane1, Benjamin Bender2, Tobias Lindig2, and Klaus Scheffler1

1High-field magnetic resonance center, Max Planck Institute for biological cybernetics, Tübingen, Germany, 2Diagnostic & Interventional Neuroradiology, University Clinic Tuebingen, Tübingen, Germany

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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