An End-to-End AI-Based Framework for Automated Discovery of CEST/MT MR Fingerprinting Acquisition Protocols and Quantitative Deep Reconstruction (AutoCEST)

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.