Automated k-Space Trajectory Generation using Bayesian Reinforcement Learning for Quiet Single Shot Readout

Acoustic noise during MR scans generated by the gradient coil vibration has been compromising for patient comfort. Single-shot echo planar imaging (EPI), ubiquitously used in functional MRI and diffusion MRI acquisitions, has a rapid switching readout gradient, which is very efficient but also very loud. In this study, we employed a model free reinforcement-learning agent to optimize 2D single shot readout gradient waveforms toward the “reward” of lowering acoustic noise. The preliminary results show that the acoustic noise of the arbitrary trajectory is 17.2 dB lower than EPI for a 2D single slice readout.