Khoubani Sahar, Moradi Mohammad Hassan
Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez, Tehran, Iran.
Phys Eng Sci Med. 2024 Dec;47(4):1691-1703. doi: 10.1007/s13246-024-01481-2. Epub 2024 Sep 12.
In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from QWT as the inputs of customized PWC-Net structure, a high-performance deep network in motion estimation. We have trained and tested our proposed method performance using two realistic simulated B-mode echocardiographic sequences. We have evaluated our proposed method in terms of both geometrical and clinical indices. Our method achieved an average endpoint error of 0.06 mm per frame and 0.59 mm between End Diastole and End Systole on a simulated dataset. Correlation analysis between ground truth and the computed strain shows a correlation coefficient of 0.89, much better than the most efficient methods in the state-of-the-art 2D echocardiography motion estimation. The results show the superiority of our proposed method in both geometrical and clinical indices.
在本文中，我们提出了一种基于二维超声心动图序列四元数小波变换（QWT）相位的新型深度学习方法，用于估计心肌的运动和应变。所提出的方法将从QWT获得的强度和相位作为定制的PWC-Net结构的输入，PWC-Net是一种用于运动估计的高性能深度网络。我们使用两个逼真的模拟B型超声心动图序列对所提出方法的性能进行了训练和测试。我们从几何和临床指标两方面对所提出的方法进行了评估。在一个模拟数据集上，我们的方法实现了每帧平均端点误差为0.06毫米，舒张末期和收缩末期之间为0.59毫米。地面真值与计算应变之间的相关性分析显示相关系数为0.89，远优于当前最先进的二维超声心动图运动估计中最有效的方法。结果表明，我们所提出的方法在几何和临床指标方面均具有优越性。
