A Deep Learning Model to Generate Customizable RF Pulse Shapes for Power Independent of Number of Slices Simultaneous Multi-Slice Imaging

Motivation: Designing radiofrequency pulses can be a challenging, time consuming, iterative process. Goal(s): Simplify the radiofrequency pulse design process using deep learning. Approach: First, a complex model was trained on a dataset of ~34k RF pulses. Second, a simpler model was trained on a dataset of 1.2M RF pulses. Both models output the characteristics needed to generate a fully sampled radiofrequency waveform. Results: Model 2 performed better than Model 1, however, the root mean squared error in expected vs. generated slice profiles on a subset of the test data was still high at 36.5%. Future work will implement an optimization loop. Impact: A fully functioning deep learning model could serve as a tool for researchers designing power independent of number of slices pulses to improve slice profiles for SMS imaging as well as novel applications such as in ex-nuclei imaging.