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Abstract #3720

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

Zhenliang Lin1, Qikang Li1, Lihong Tang1, Hui Huang1, Junwei Zhao1, and Jie Luo1
1Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

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.

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