  Purpose: To develop an automated machine-learning-based method for the
discovery of rapid and quantitative chemical exchange saturation transfer
(CEST) MR fingerprinting acquisition and reconstruction protocols.
  Methods: An MR physics governed AI system was trained to generate optimized
acquisition schedules and the corresponding quantitative reconstruction
neural-network. The system (termed AutoCEST) is composed of a CEST saturation
block, a spin dynamics module, and a deep reconstruction network, all
differentiable and jointly connected. The method was validated using a variety
of chemical exchange phantoms and an in-vivo mouse brain at 9.4T.
  Results: The acquisition times for AutoCEST optimized schedules ranged from
35-71s, with a quantitative image reconstruction time of only 29 ms. The
resulting exchangeable proton concentration maps for the phantoms were in good
agreement with the known solute concentrations for AutoCEST sequences (mean
absolute error = 2.42 mM; Pearson's r=0.992 , p$<$0.0001), but not for an
unoptimized sequence (mean absolute error = 65.19 mM; Pearson's r=-0.161,
p=0.522). Similarly, improved exchange rate agreement was observed between
AutoCEST and quantification of exchange using saturation power (QUESP) methods
(mean absolute error: 35.8 Hz, Pearson's r=0.971, p$<$0.0001) compared to an
unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson's
r=0.959, p$<$0.0001). The AutoCEST in-vivo mouse brain semi-solid proton
volume-fractions were lower in the cortex (12.21$\pm$1.37%) compared to the
white-matter (19.73 $\pm$ 3.30%), as expected, and the amide proton
volume-fraction and exchange rates agreed with previous reports.
  Conclusion: AutoCEST can automatically generate optimized CEST/MT acquisition
protocols that can be rapidly reconstructed into quantitative exchange
parameter maps.
