DeepRF1 is a recently proposed RF pulse design method using deep reinforcement learning and optimization, generating RF defined by a reward (e.g., slice profile and energy constraint) from self-learning. Here, we proposed an accelerated algorithm for DeepRF that utilizes a modified optimal control, replacing the computationally complex gradient ascent-based optimizer. The new algorithm is tested for slice-selective inversion and slice-selective excitation and compared with original DeepRF and SLR RF pulses, reporting improved computation efficiency while preserving performances. Additionally, a short-duration B1-insensitive inversion pulse, which was difficult to produce in conventional RF algorithms, is designed to demonstrate the usefulness of DeepRF.
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