基于强化学习的自适应k空间径向采样用于心脏MRI
Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning
AI总结：
本文提出一种基于强化学习的双分支框架，结合交叉注意力与解剖感知奖励，优化心脏MRI径向k空间采样轨迹，在多个加速因子下提升重建质量。
摘要：
加速磁共振成像（MRI）需要对k空间采样模式进行精细优化，以平衡采集速度与图像质量。尽管近期深度学习在优化笛卡尔采样方面展现出潜力，但强化学习（RL）在非笛卡尔轨迹优化中的潜力仍未得到充分探索。在本工作中，我们提出了一种新颖的RL框架，用于优化心脏MRI中的径向采样轨迹。我们的方法采用双分支架构，联合处理k空间和图像域信息，并结合交叉注意力融合机制以促进域间有效的信息交换。该框架采用解剖感知的奖励设计和黄金比例采样策略，以确保均匀的k空间覆盖，同时保留心脏结构细节。实验结果表明，我们的方法在多个加速因子下有效学习了最优径向采样策略，与常规方法相比，实现了更优的重建质量。代码可在 https://github.com/Ruru-Xu/RL-kspace-Radial-Sampling 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
