Deep reinforcement learning-designed radiofrequency waveform in MRI
This work addresses the challenge of RF pulse design in MRI, offering a potential AI-driven approach that could discover new mechanisms beyond human intuition, though it appears incremental as it builds on existing methods in a specific domain.
The authors tackled the inverse problem of designing radiofrequency waveforms in MRI by proposing DeepRF, a deep reinforcement learning framework that generated novel RF pulses, which successfully met design criteria and reduced energy in four common pulse types.
Carefully engineered radiofrequency (RF) pulses play a key role in a number of systems such as mobile phone, radar, and magnetic resonance imaging. The design of an RF waveform, however, is often posed as an inverse problem with no general solution. As a result, various design methods each with a specific purpose have been developed based on the intuition of human experts. In this work, we propose an artificial intelligence (AI)-powered RF pulse design framework, DeepRF, which utilizes the self-learning characteristics of deep reinforcement learning to generate a novel RF pulse. The effectiveness of DeepRF is demonstrated using four types of RF pulses that are commonly used. The DeepRF-designed pulses successfully satisfy the design criteria while reporting reduced energy. Analyses demonstrate the pulses utilize new mechanisms of magnetization manipulation, suggesting the potentials of DeepRF in discovering unseen design dimensions beyond human intuition. This work may lay the foundation for an emerging field of AI-driven RF waveform design.