Punithakumar Kumaradevan, Ben Ayed Ismail, Soliman Abraam S, Goela Aashish, Islam Ali, Li Shuo, Noga Michelle
1Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABT6G 2R3Canada.
2Servier Virtual Cardiac CentreMazankowski Alberta Heart InstituteEdmontonABT6G 2B7Canada.
IEEE J Transl Eng Health Med. 2020 Apr 24;8:1800209. doi: 10.1109/JTEHM.2020.2989390. eCollection 2020.
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.
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.
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.
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.
本研究利用在常规成像过程中采集的不同二维（2D）心脏磁共振（CMR）序列的融合来研究三维（3D）左心室（LV）运动的估计。尽管标准临床电影CMR数据本质上是二维的，但实际潜在的左心室动力学存在于三维空间中，无法通过单个二维CMR图像序列完全捕捉。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三维空间中心肌边界的位置和速度信息组成的动态状态向量。
所提出的方法包括两个主要部分：在二维CMR序列中跟踪心肌点以及融合与跟踪点对应的多个轨迹。使用微分同胚非刚性图像配准方法进行跟踪，该跟踪产生表示心肌点的一组相应时间点。然后，使用无味卡尔曼平滑器（UKS）和轨迹到轨迹融合算法，将从每个电影CMR序列获得的轨迹与来自其他CMR视图的相应轨迹进行融合。
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。我们通过将获得的状态向量估计投影到从同一受试者获取的二维标记CMR图像上，并将它们与谐波相位估计进行比较，报告了定量性能分析。在所提出的算法在从30名受试者获取的118个图像序列上进行评估时，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）。
本研究表明，融合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，融合来自长轴和短轴视图的组织跟踪信息可改善自动区域功能评估的二元分类。
