Keywords: RF Pulse Design & Fields, RF Pulse Design & Fields, Reinforcement Learning
Motivation: DeepRF framework demonstrates superior capabilities in designing radiofrequency pulses using deep reinforcement learning, however its high computational demands limit its current use in clinical and industrial settings.
Goal(s): Our objective is to reduce the computational time while maintaining design quality and to overcome the original framework’s need for millions of pulse candidates per design.
Approach: We propose FastDeepRF, a redesigned reinforcement learning architecture that observes magnetization states in real-time before deciding actions and leverages distributed training.
Results: Computation time decreased from 40 hours to 2.5 hours, achieving improved pulse design quality while requiring only 10,000 pulse candidates instead of 3.8 million.
Impact: FastDeepRF’s dramatic reduction in computation time and increased efficiency open the way to broader adoption of AI-designed RF pulses in clinical and industrial settings, enhancing MRI exam outcomes through reduced SAR and enhanced image quality.
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