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Optimization of Doppler velocity echocardiographic measurements using an automatic contour detection method

机译:使用自动轮廓检测方法优化多普勒速度超声心动图测量

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Intra- and inter-observer variability in Doppler velocity echocardiographic measurements (DVEM) is a significant issue. Indeed, imprecisions of DVEM can lead to diagnostic errors, particularly in the quantification of the severity of heart valve dysfunction. To minimize the variability and rapidity of DVEM, we have developed an automatic method of Doppler velocity wave contour detection, based on active contour models. To validate our new method, results obtained with this method were compared to those obtained manually by an experienced echocardiographer on Doppler echocardiographic images of left ventricular outflow tract and transvalvular flow velocity signals recorded in 30 patients, 15 with aortic stenosis and 15 with mitral stenosis. We focused on three essential variables that are measured routinely by Doppler echocardiography in the clinical setting: the maximum velocity, the mean velocity and the velocity-time integral. Comparison between the two methods has shown a very good agreement (linear correlation coefficient R~2 = 0.99 between the automatically and the manually extracted variables). Moreover, the computation time was really short, about 5s. This new method applied to DVEM could, therefore, provide a useful tool to eliminate the intra- and inter-observer variabilities associated with DVEM and thereby to improve the diagnosis of cardiovascular disease. This automatic method could also allow the echocardiographer to realize these measurements within a much shorter period of time compared to standard manual tracing method. From a practical point of view, the model developed can be easily implanted in a standard echocardiographic system.
机译:多普勒速度超声心动图测量(DVEM)中的观察者和观察者间变异性是一个重要问题。实际上,DEM的不精确可能导致诊断误差,特别是在量化心脏瓣膜功能障碍的严重程度的情况下。为了最大限度地减少DEM的变异性和快速性,我们开发了一种基于主动轮廓模型的多普勒速度波轮廓检测的自动方法。为了验证我们的新方法,将使用该方法获得的结果与经验丰富的超声心动仪对左心室流出道的多普勒超声心动图和30名患者中记录的分子流速信号手动获得的结果进行比较,其中15名患者有15例,具有二尖瓣狭窄的15例。我们专注于三个基本变量,临床环境中的多普勒超声心动图常规测量:最大速度,平均速度和速度 - 时间积分。两种方法之间的比较显示了非常好的一致性(线性相关系数R〜2 = 0.99在自动和手动提取的变量之间)。而且,计算时间真的很短,大约为5s。因此,这种应用于DEM的新方法可以提供一种有用的工具,以消除与DEM相关的内部观察者和观察者间变形性,从而改善心血管疾病的诊断。与标准手动跟踪方法相比,这种自动方法还可以允许超声心电图计算机在更短的时间内实现这些测量。从实际的角度来看,开发的模型可以很容易地植入标准超声心动图系统。

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