Deep reinforcement learning-designed radiofrequency waveform in MRI

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 on the basis of 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. Radiofrequency pulses of different shapes can increase the efficiency of applications such as broadcasting or medical imaging, but finding the optimal shape for a specific use can be computationally costly. Shin and colleagues present a new method based on deep reinforcement learning to design radiofrequency pulses for use in MRI, which is demonstrated to cover different types of optimization goals for each application.