Abstract #2932

Investigating a residual neural network to generated customizable RF pulses for MRI

Seger Nelson¹ and Rebecca Emily Feldman^2,3

¹Computer Science, Mathematics, Physics, and Statistics, University of British Columbia, Kelowna, BC, Canada, ²Computer Science, Mathematics, Physics, and Statistics, University of British Columbia Okanagan, Kelowna, BC, Canada, ³Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Synopsis

We investigate a method for the design of radio frequency (RF) pulses for custom simultaneous multi slice spatial profiles. We trained a variation of residual neural networks on a dataset of 96,000 generated adiabatic “power independent of the number of slices” (PINS) RF pulses, varying parameters such as gradient duty cycle, number of pulse lobes, quadratic phase strength, and transition and filter bandwidth. We compared the direct-design RF pulses to those generated by our model.

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