Deep reinforcement learning-designed radiofrequency waveform in MRI

TL;DR

This paper introduces DeepRF, an AI-based deep reinforcement learning framework for designing RF pulses in MRI, capable of generating novel, efficient waveforms that outperform traditional methods and reveal new magnetization manipulation mechanisms.

Contribution

The paper presents the first application of deep reinforcement learning to RF pulse design in MRI, enabling automatic discovery of innovative waveforms beyond human intuition.

Findings

DeepRF-designed pulses meet design criteria and reduce energy consumption.

Pulses utilize new mechanisms of magnetization manipulation.

DeepRF demonstrates potential for discovering unseen design dimensions.

Abstract

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.