用强化学习优化MRI指纹成像的翻转角序列，提升图像区分度并可能加速扫描。
Optimize Flip Angle Schedules In MR Fingerprinting Using Reinforcement Learning
- 采用强化学习自动设计翻转角时序，解决高维参数优化难题。
- 学习到非周期性翻转角模式，显著提升指纹可区分性。
- 优化后或可减少重复时间，适合追求高效MRI成像的研究者。
磁共振指纹成像（MRF）利用可调采集参数产生的瞬态信号动态，使最优序列设计成为复杂的高维序列决策问题，例如翻转角这一关键参数的优化。强化学习（RL）为自动化参数选择提供了新路径，可优化脉冲序列以最大化指纹在参数空间中的可区分性。本文提出一种基于强化学习的翻转角时序优化框架，并展示了学习得到的非周期性翻转角序列能有效增强指纹分离能力。此外，一个有趣发现是：经强化学习优化的序列可能减少重复时间（repetition time），从而潜在加速MRF扫描过程。
原文摘要 · Abstract (English)
Magnetic Resonance Fingerprinting (MRF) leverages transient-state signal dynamics generated by the tunable acquisition parameters, making the design of an optimal, robust sequence a complex, high-dimensional sequential decision problem, such as optimizing one of the key parameters, flip angle. Reinforcement learning (RL) offers a promising approach to automate parameter selection, to optimize pulse sequences that maximize the distinguishability of fingerprints across the parameter space. In this work, we introduce an RL framework for optimizing the flip-angle schedule in MRF and demonstrate a learned schedule exhibiting non-periodic patterns that enhances fingerprint separability. Additionally, an interesting observation is that the RL-optimized schedule may enable a reduction in the number of repetition time, potentially accelerate MRF acquisitions.
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