Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3891-3894. doi: 10.1109/EMBC48229.2022.9872008.
The global longitudinal strain of the myocardial tissue has been shown to be a better indicator of cardiac pathologies in the subclinical stage than other indices, such as the ejection fraction. This article presents a new deep learning approach for strain estimation in 2D echocardiograms. The proposed method improves the performance of the state of the art without losing stability with noisy echocardiograms and achieved an average end point error of 0.14 ± 0.17 pixels in the estimation of the optical flow in the myocardium and an error of 1.34 ± 2.34 % in the estimation of the global longitudinal strain indicator when evaluated in a synthetic echocardiographic dataset. Further research will validate the proposed method by a clinical in-vivo dataset. Clinical relevance- This paper presents a method to estimate the global longitudinal strain index in noisy echocardiograms, which promises to be a better indicator of cardiac pathologies in the subclinical stage than other indices such as the ejection fraction.
心肌组织的整体纵向应变已被证明是一种比射血分数等其他指标更好的亚临床心脏病变的指示物。本文提出了一种新的用于二维超声心动图应变估计的深度学习方法。所提出的方法在不失去稳定性的情况下提高了现有技术的性能，并且在合成超声心动数据集上对心肌的光流进行估计时，平均端点误差为 0.14±0.17 像素，对整体纵向应变指标的估计误差为 1.34±2.34%。进一步的研究将通过临床体内数据集来验证所提出的方法。临床相关性-本文提出了一种在噪声超声心动图中估计整体纵向应变指标的方法，有望成为比射血分数等其他指标更好的亚临床阶段心脏病变的指示物。
