Echodynamography (EDG) is a computational method to estimate and visualize two-dimensional flow velocity vectors by applying dynamic flow theories to color Doppler echocardiography. The EDG method must be validated if applied to human cardiac flow function. However, a few studies of flow estimated have compared by EDG to the flow data were acquired by other methods. In this study, EDG was validated by comparing the analysis of estimating and visualizing flow velocity vectors obtained by original particle image velocimetry (PIV) based on a left ventricular (LV) phantom hydrogel (in vitro studies) and by EDG based on the virtual Doppler velocity. Velocity measured by PIV method and velocity estimated by EDG method in the perpendicular direction and the radial direction were compared. Regression analysis for the velocity estimated in the radial direction revealed an excellent correlation ( R 2 = 0 . 99 , slope = 0.96) and moderate correlation in the perpendicular direction ( R 2 = 0 . 44 , slope = 0.46). As revealed by the Bland–Altman plot, however, overestimations and higher relative error were observed in the perpendicular direction (0.51 ± 2.75 mm/s) and in the radial direction (–2.15 ± 21.13 mm/s). The percentage error of the norm-wise relative error of the velocity discrepancy is less than 10 % , and velocity magnitude followed the same trends and are of comparable magnitude. These findings indicate that good estimates of velocity can be obtained by the EDG method. Therefore, the EDG method was appropriate for estimating and visualizing velocity vectors in clinical studies for higher measurement accuracy and reliability. The clinical in vivo application showed that the EDG method has the ability to visualize blood flow velocity vectors and differentiate the clinical information of vortex parameters both in normal and abnormal LV subjects. In conclusion, the EDG method has potentially greater clinical acceptance as a tool assessment of LV during the cardiac cycle.

中文翻译：

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与粒子图像测速法相比，回波测力法的验证

超声动态成像 (EDG) 是一种计算方法，通过将动态血流理论应用于彩色多普勒超声心动图来估计和可视化二维流速矢量。如果将 EDG 方法应用于人类心脏血流功能，则必须对其进行验证。然而，一些关于流量估计的研究已经将 EDG 与通过其他方法获得的流量数据进行了比较。在这项研究中，通过比较基于左心室 (LV) 幻影水凝胶（体外研究）的原始粒子图像测速 (PIV) 和基于虚拟多普勒的 EDG 获得的估计和可视化流速矢量的分析，验证了 EDG速度。将PIV法测得的速度与EDG法测得的垂直方向和径向速度进行比较。径向估计速度的回归分析揭示了极好的相关性（R 2 = 0 . 99，斜率= 0.96）和垂直方向的中等相关性（R 2 = 0 . 44，斜率= 0.46）。然而，正如 Bland-Altman 图所揭示的那样，在垂直方向 (0.51 ± 2.75 mm/s) 和径向 (–2.15 ± 21.13 mm/s) 上观察到高估和更高的相对误差。速度差异的范数相对误差的百分比误差小于 10 % ，速度大小遵循相同的趋势并且具有可比性。这些发现表明 EDG 方法可以获得良好的速度估计。因此，EDG 方法适用于估计和可视化临床研究中的速度矢量，以获得更高的测量精度和可靠性。临床体内应用表明，EDG 方法具有可视化血流速度矢量和区分正常和异常 LV 受试者的涡流参数临床信息的能力。总之，EDG 方法作为心动周期中 LV 的工具评估具有潜在的更大临床接受度。