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Abstract #2928

Accelerated DeepRF using modified optimal control

Jiye Kim1, Dongmyung Shin1, Hongjun An1, Hwihun Jeong1, Minjun Kim1, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of


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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