Keywords: Pulse Sequence Design, Machine Learning/Artificial Intelligence
DeepRF1 is a recently proposed RF pulse design method2-5 using deep reinforcement learning and optimization, generating an RF pulse defined by a reward (e.g., slice profile and energy constraint) from self-learning. Here, we proposed an improved algorithm for DeepRF that incorporates a modulation function to design an simultaneous multislice6 RF pulse. The new algorithm is tested and compared with the original multiband9 pulses, reporting reduced RF energy while preserving the characteristics of the original slice profile. Additionally, a multiPINS8 like inversion pulse is designed to demonstrate the usefulness of DeepRF for a non-constant slice selective gradient.
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