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Abstract #4421

FastDeepRF: Accelerated AI-Driven Radiofrequency Pulse Design through Enhanced Reinforcement Learning and Distributed Computing

Rohitkumar Datchanamourty1, Kyonghyun Min2, Jongho Lee1, and Min-hwan Oh2
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Graduate School of Data Science, Seoul National University, Seoul, Korea, Republic of

Synopsis

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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