Chemical exchange saturation transfer MR fingerprinting (CEST-MRF) enables quantification of multiple tissue parameters. Optimization of the acquisition schedule can improve tissue discrimination and reduce scan times but is highly challenging because of the large number of acquisition and tissue parameters. The goal of this work is to demonstrate a scalable deep learning based global optimization method that provides schedules with improved discrimination. The benefits of our approach are demonstrated in an in vivo mouse tumor model.
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