  Purpose: To substantially shorten the acquisition time required for
quantitative 3D chemical exchange saturation transfer (CEST) and semisolid
magnetization transfer (MT) imaging and allow for rapid chemical exchange
parameter map reconstruction. Methods: Three-dimensional CEST and MT magnetic
resonance fingerprinting (MRF) datasets of L-arginine phantoms, whole-brains,
and calf muscles from healthy volunteers, cancer patients, and cardiac patients
were acquired using 3T clinical scanners at 3 different sites, using 3
different scanner models and coils. A generative adversarial network supervised
framework (GAN-CEST) was then designed and trained to learn the mapping from a
reduced input data space to the quantitative exchange parameter space, while
preserving perceptual and quantitative content. Results: The GAN-CEST 3D
acquisition time was 42-52 seconds, 70% shorter than CEST-MRF. The quantitative
reconstruction of the entire brain took 0.8 seconds. An excellent agreement was
observed between the ground truth and GAN-based L-arginine concentration and pH
values (Pearson's r > 0.97, NRMSE < 1.5%). GAN-CEST images from a brain-tumor
subject yielded a semi-solid volume fraction and exchange rate NRMSE of
3.8$\pm$1.3% and 4.6$\pm$1.3%, respectively, and SSIM of 96.3$\pm$1.6% and
95.0$\pm$2.4%, respectively. The mapping of the calf-muscle exchange parameters
in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi-solid
exchange parameters. In regions with large susceptibility artifacts, GAN-CEST
has demonstrated improved performance and reduced noise compared to MRF.
Conclusion: GAN-CEST can substantially reduce the acquisition time for
quantitative semisolid MT/CEST mapping, while retaining performance even when
facing pathologies and scanner models that were not available during training.
