Abstract #1451

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

Leonie E. Hunger¹, Alexander German¹, Felix Glang², Katrin M. Khakzar¹, Nam Dang¹, Angelika Mennecke¹, Andreas Maier³, Frederik Laun⁴, and Moritz Zaiss^1,2

¹Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ²High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ³Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany, ⁴Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

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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