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 in vivo mouse brains at 9.4T. Results: The acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, 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 (Formula presented.), (Formula presented.)), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson’s (Formula presented.), (Formula presented.)). 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 (Formula presented.), (Formula presented.)) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson’s (Formula presented.), (Formula presented.)). The AutoCEST in vivo mouse brain semi-solid proton volume fractions were lower in the cortex (12.77% ± 0.75%) compared to the white matter (19.80% ± 0.50%), as expected. Conclusion: AutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.
- chemical exchange saturation transfer (CEST)
- deep learning
- magnetic resonance fingerprinting (MRF)
- magnetization transfer (MT)
- quantitative imaging