Optimize Flip Angle Schedules In MR Fingerprinting Using Reinforcement Learning

ABSTRACTMagnetic Resonance Fingerprinting (MRF) leverages transient-state signal dynamics generated by the tunable acquisition parameters, making the design of an optimal, robust sequence a complex, high-dimensional sequential decision problem, such as optimizing one of the key parameters, flip angle. Reinforcement learning (RL) offers a promising approach to automate parameter selection, to optimize pulse sequences that maximize the distinguishability of fingerprints across the parameter space. In this work, we introduce an RL framework for optimizing the flip-angle schedule in MRF and demonstrate a learned schedule exhibiting non-periodic patterns that enhances fingerprint separability. Additionally, an interesting observation is that the RL-optimized schedule may enable a reduction in the number of repetition time, potentially accelerate MRF acquisitions. INTRODUCTIONThe acquisition phase of Magnetic Resonance Fingerprinting(MRF), characterized by the pseudo-random variation of parameters, presents a sophisticated optimal control problem. Some works use empirical sequence design for MRF acquisition, like QALAS that uses an interleaved Look-locker sequence with T2 preparation pulse.