The equations of motion that govern nuclear magnetic resonance lead to an incredible variety of MRI contrast mechanisms and spatial encoding schemes to be accessed via the application of cleverly constructed sequences of applied magnetic fields. However, the full potential of the Bloch equations has been difficult to exploit due to their non-intuitive, nonlinear dynamics which can devolve into chaotic behaviors and otherwise have intractable, non-analytical solutions1. Our previous work4 introduced a model-free reinforcement learning approach to pulse sequence generation, with an AI agent that explores an unknown MR imaging environment with pulse sequence “actions,” and constructs a model through corresponding RF receive-signal “rewards.” In this work, we demonstrate the same AI agent learning to generate optimal RF waveforms to perform slice selection in unknown inhomogeneous B0 settings.
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