Ferraz Sofia, Coimbra Miguel, Pedrosa Joao
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782542.
Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of measurements retrieved from images. In this study, deep learning-based motion estimation architectures were used to determine the left ventricular longitudinal strain in echocardiography. Three motion estimation approaches, pretrained on popular optical flow datasets, were applied to a simulated echocardiographic dataset. Results show that PWC-Net, RAFT and FlowFormer achieved an average end point error of 0.20, 0.11 and 0.09 mm per frame, respectively. Additionally, global longitudinal strain was calculated from the FlowFormer outputs to assess strain correlation. Notably, there is variability in strain accuracy among different vendors. Thus, optical flow-based motion estimation has the potential to facilitate the use of strain imaging in clinical practice.
在评估心脏功能和计算心肌变形指数时，超声心动图中的运动估计至关重要。然而，临床实践中存在局限性，特别是从图像中获取的测量值的准确性和可靠性方面。在本研究中，基于深度学习的运动估计架构被用于确定超声心动图中的左心室纵向应变。三种在流行光流数据集上预训练的运动估计方法被应用于模拟超声心动图数据集。结果表明，PWC-Net、RAFT和FlowFormer每帧的平均终点误差分别为0.20、0.11和0.09毫米。此外，根据FlowFormer的输出计算全局纵向应变以评估应变相关性。值得注意的是，不同供应商之间的应变准确性存在差异。因此，基于光流的运动估计有潜力促进应变成像在临床实践中的应用。
