IEEE Trans Biomed Eng. 2020 Jun;67(6):1727-1738. doi: 10.1109/TBME.2019.2944803. Epub 2019 Sep 30.
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.
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.
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.
The proposed fusion-based respiratory motion prediction framework using golden-angle radial MRI can achieve low-latency feedback with improved accuracy.
Respiratory motion prediction using the fusion-based method can overcome system latency to provide accurate feedback information for MRI-guided interventions.
磁共振成像（MRI）可为呼吸影响器官（如肝脏）的介入提供指导。本研究旨在：1）利用实时黄金角度径向 MRI 研究基于图像和基于替代物的运动跟踪方法；2）提出并评估一种新的基于融合的多速率卡尔曼滤波呼吸运动预测框架。
从同一黄金角度径向 MRI 数据流中重建具有不同时间足迹的图像，以同时以 10 Hz 实现基于图像和基于替代物的跟踪。构建了一个定制的软件管道，用于进行在线跟踪，并使用可编程运动体模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，以结合基于图像跟踪的较低跟踪误差和基于替代物跟踪的较低延迟。该基于融合的方法在使用体内实时自由呼吸肝脏 MRI 的回顾性研究中进行了评估。
体模实验证实，基于图像的跟踪的在线跟踪中位误差低于基于替代物的方法，但中位系统延迟较高（350 ms 比 150 ms）。在回顾性的体内研究中，分析了来自 8 名受试者的 75 个目标特征的呼吸波形。基于融合的方法的中位均方根预测误差（RMSE）（0.97mm）低于基于替代物（1.18mm）和基于图像（1.3mm）的方法（Wilcoxon 符号秩检验，p < 0.05）。
使用黄金角度径向 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。
基于融合的方法的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。
