Objective: Magnetic resonance imaging (MRI) can provide guidance for interventions in organs affected by respiration (e.g., liver). This study aims to: 1) investigate image-based and surrogate-based motion tracking methods using real-time golden-angle radial MRI; and 2) propose and evaluate a new fusion-based respiratory motion prediction framework with multi-rate Kalman filtering. Methods: Images with different temporal footprints were reconstructed from the same golden-angle radial MRI data stream to simultaneously enable image-based and surrogate-based tracking at 10 Hz. A custom software pipeline was constructed to perform online tracking and calibrate tracking error and latency using a programmable motion phantom. A fusion-based motion prediction method was developed to combine the lower tracking error of image-based tracking with the lower latency of surrogate-based tracking. The fusion-based method was evaluated in retrospective studies using in vivo real-time free-breathing liver MRI. Results: Phantom experiments confirmed that the median online tracking error of image-based tracking was lower than surrogate-based methods, however, with higher median system latency (350 ms vs. 150 ms). In retrospective in vivo studies, 75 respiratory waveforms of target features from eight subjects were analyzed. The median root-mean-squared prediction error (RMSE) for the fusion-based method (0.97 mm) was reduced (Wilcoxon signed rank test p < 0.05) compared to surrogate-based (1.18 mm) and image-based (1.3 mm) methods. Conclusion: The proposed fusion-based respiratory motion prediction framework using golden-angle radial MRI can achieve low-latency feedback with improved accuracy. Significance: Respiratory motion prediction using the fusion-based method can overcome system latency to provide accurate feedback information for MRI-guided interventions.

中文翻译：

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使用基于融合的多速率卡尔曼滤波和实时黄金角径向 MRI 进行呼吸运动预测

目的：磁共振成像（MRI）可以为受呼吸影响的器官（例如肝脏）的干预提供指导。本研究旨在：1) 使用实时黄金角径向 MRI 研究基于图像和基于代理的运动跟踪方法；和 2) 提出并评估一种新的基于融合的呼吸运动预测框架，具有多速率卡尔曼滤波。方法：从相同的黄金角度径向 MRI 数据流重建具有不同时间足迹的图像，以同时启用 10 Hz 的基于图像和基于代理的跟踪。构建了一个定制的软件管道，以使用可编程运动体模执行在线跟踪和校准跟踪误差和延迟。开发了一种基于融合的运动预测方法，以将基于图像的跟踪的较低跟踪误差与基于代理的跟踪的较低延迟相结合。在回顾性研究中使用体内实时自由呼吸肝脏 MRI 评估了基于融合的方法。结果：Phantom 实验证实，基于图像的跟踪的中值在线跟踪误差低于基于代理的方法，但具有更高的中值系统延迟（350 毫秒与 150 毫秒）。在回顾性体内研究中，分析了来自 8 名受试者的 75 种目标特征的呼吸波形。与基于代理 (1.18 mm) 和基于图像 (1.3 mm) 的方法相比，基于融合的方法 (0.97 mm) 的中值均方根预测误差 (RMSE) 降低（Wilcoxon 符号秩检验 p < 0.05）方法。结论：所提出的使用黄金角径向 MRI 的基于融合的呼吸运动预测框架可以实现低延迟反馈并提高准确性。意义：使用基于融合的方法进行呼吸运动预测可以克服系统延迟，为 MRI 引导的干预提供准确的反馈信息。