Gradient Preemphasis Predicted by Reinforcement Learning

Motivation: Gradient hardware chains can exhibit dynamic nonlinearities that cannot be easily corrected with linear models and require more sophisticated approaches. Goal(s): Our goal was to develop a flexible and dynamic approach to correct nonlinear MRI system imperfections. Approach: We developed a reinforcement learning method for predicting gradient preemphasis and evaluated it in a realistic simulated environment with obscured state information. Results: Reinforcement learning is able to accurately predict gradient preemphasis even when system state information is unknown. Impact: The ability to dynamically correct system imperfections through reinforcement learning may allow the development of more robust imaging systems that can adapt to complex, nonlinear distortions, reducing the need for expensive hardware corrections or inflexible, system-specific system models.