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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here