用强化学习优化心脏MRI的径向采样，提升加速成像质量。
Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning
- 基于双分支架构融合k空间与图像域信息，通过交叉注意力协同优化
- 在不同加速倍数下学习最优采样策略，重建质量优于传统方法
- 结合解剖感知奖励与黄金比例采样，兼顾覆盖均匀性与结构细节
加速磁共振成像（MRI）需要精心优化k空间采样模式以平衡采集速度与图像质量。尽管深度学习在笛卡尔采样优化中取得进展，但强化学习（RL）在非笛卡尔轨迹优化中的潜力仍待探索。本文提出一种新颖的强化学习框架，用于优化心脏MRI的径向采样轨迹。该框架采用双分支结构，联合处理k空间与图像域信息，并引入交叉注意力融合机制，促进两域间有效信息交换。通过解剖感知奖励设计与黄金比例采样策略，确保k空间覆盖均匀且保留心脏结构细节。实验结果表明，该方法在多种加速因子下均能有效学习最优径向采样策略，重建质量显著优于传统方法。代码已开源：https://github.com/Ruru-Xu/RL-kspace-Radial-Sampling
原文摘要 · Abstract (English)
Accelerated Magnetic Resonance Imaging (MRI) requires careful optimization of k-space sampling patterns to balance acquisition speed and image quality. While recent advances in deep learning have shown promise in optimizing Cartesian sampling, the potential of reinforcement learning (RL) for non-Cartesian trajectory optimization remains largely unexplored. In this work, we present a novel RL framework for optimizing radial sampling trajectories in cardiac MRI. Our approach features a dual-branch architecture that jointly processes k-space and image-domain information, incorporating a cross-attention fusion mechanism to facilitate effective information exchange between domains. The framework employs an anatomically-aware reward design and a golden-ratio sampling strategy to ensure uniform k-space coverage while preserving cardiac structural details. Experimental results demonstrate that our method effectively learns optimal radial sampling strategies across multiple acceleration factors, achieving improved reconstruction quality compared to conventional approaches. Code available: https://github.com/Ruru-Xu/RL-kspace-Radial-Sampling
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