Objective: This study investigates the estimation of three dimensional (3D) left ventricular (LV) motion using the fusion of different two dimensional (2D) cine magnetic resonance (CMR) sequences acquired during routine imaging sessions. Although standard clinical cine CMR data is inherently 2D, the actual underlying LV dynamics lies in 3D space and cannot be captured entirely using single 2D CMR image sequences. By utilizing the image information from various short-axis and long-axis image sequences, the proposed method intends to estimate the dynamic state vectors consisting of the position and velocity information of the myocardial borders in 3D space. Method: The proposed method comprises two main components: tracking myocardial points in 2D CMR sequences and fusion of multiple trajectories correspond to the tracked points. The tracking which yields the set of corresponding temporal points representing the myocardial points is performed using a diffeomorphic nonrigid image registration approach. The trajectories obtained from each cine CMR sequence is then fused with the corresponding trajectories from other CMR views using an unscented Kalman smoother (UKS) and a track-to-track fusion algorithm. Results: We evaluated the proposed method by comparing the results against CMR imaging with myocardial tagging. We report a quantitative performance analysis by projecting the state vector estimates we obtained onto 2D tagged CMR images acquired from the same subjects and comparing them against harmonic phase estimates. The proposed algorithm yielded a competitive performance with a mean root mean square error of 1.3±0.5 pixels (1.8±0.6 mm) evaluated over 118 image sequences acquired from 30 subjects. Conclusion: This study demonstrates that fusing the information from short and long-axis views of CMR improves the accuracy of cardiac tissue motion estimation. Clinical Impact: The proposed method demonstrates that the fusion of tissue tracking information from long and short-axis views improves the binary classification of the automated regional function assessment.

中文翻译：

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使用 MRI 和 Track-to-Track Fusion 对左心室动力学进行 3D 运动估计

目的：本研究使用在常规成像会话期间获得的不同二维 (2D) 电影磁共振 (CMR) 序列的融合来研究三维 (3D) 左心室 (LV) 运动的估计。尽管标准的临床电影 CMR 数据本质上是 2D 的，但实际潜在的 LV 动态位于 3D 空间中，无法使用单个 2D CMR 图像序列完全捕获。通过利用来自各种短轴和长轴图像序列的图像信息，所提出的方法旨在估计由 3D 空间中心肌边界的位置和速度信息组成的动态状态向量。方法：所提出的方法包括两个主要部分：跟踪二维 CMR 序列中的心肌点和与跟踪点对应的多个轨迹的融合。使用微分非刚性图像配准方法执行产生代表心肌点的相应时间点集的跟踪。然后，使用无迹卡尔曼平滑器 (UKS) 和轨道到轨道融合算法，将从每个电影 CMR 序列获得的轨迹与来自其他 CMR 视图的相应轨迹融合。结果：我们通过将结果与带有心肌标记的 CMR 成像进行比较来评估所提出的方法。我们通过将我们获得的状态向量估计投影到从相同对象获取的 2D 标记 CMR 图像上并将它们与谐波相位估计进行比较来报告定量性能分析。所提出的算法产生了具有竞争力的性能，平均均方根误差为 1.3±0.5 像素 (1.8±0. 6 毫米）评估了从 30 名受试者获得的超过 118 个图像序列。结论：本研究表明，融合来自 CMR 短轴和长轴视图的信息可提高心脏组织运动估计的准确性。临床影响：所提出的方法表明，来自长轴和短轴视图的组织跟踪信息的融合改进了自动区域功能评估的二元分类。