  Purpose: Separation of different CEST signals in the Z-spectrum is a
challenge especially at low field strengths where amide, amine, and NOE peaks
coalesce with each other or with the water peak. The purpose of this work is to
investigate if the information in 3T spectra can be extracted by a deep
learning approach trained by 9.4T human brain target data. Methods:
Highly-spectrally-resolved Z-spectra from the same volunteer were acquired by
3D-snapshot CEST MRI at 3 T and 9.4 T with similar saturation schemes. The
volume-registered 3 T Z-spectra-stack was then used as input data for a 3-layer
deep neural network with the volume-registered 9.4 T fitted parameter stack as
target data. The neural network was optimized and applied to training data, to
unseen data from a different volunteer, and as well to a tumor patient data
set. Results: A useful neural net architecture could be found and verified in
healthy volunteers. The principle gray-/white matter contrast of the different
CEST effects was predicted with only small deviations. The 9.4 T prediction was
less noisy compared to the directly measured CEST maps, however at the cost of
slightly lower tissue contrast. Application to a tumor patient measured at 3 T
and 9.4 T revealed that tumorous tissue Z-spectra and corresponding
hyper/hypo-intensities of different CEST effects can also be predicted.
Conclusion: Deep learning might be a powerful tool for CEST data processing and
deepCEST could bring the benefits and insights of the few ultra-high field
sites to a broader clinical use. Vice versa deepCEST might help for determining
which subjects are good candidates to measure additionally at UHF.
