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Automated cardiac time interval measurement for Modified Myocardial Performance Index calculation of right ventricle

机译:自动测量心脏时间间​​隔以计算右心室的修正心肌性能指数

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The Modified Myocardial Performance Index (Mod-MPI) has sparked great interest as a parameter for fetal cardiac function assessment. However, measurement of this index requires expertise and its clinical application might be limited, owing to its poor repeatability. Research groups have been investigating left Mod-MPI (that is, Mod-MPI from left ventricle valve events), and an automated algorithm has been developed for left Mod-MPI calculation in our previous study. Right MPI is also important as it becomes abnormal earlier than left MPI in some pathologies; however, for use across the gestational age spectrum, it requires two-image acquisition. This paper presents an automated method to detect valve movements during atrioventricular outflow and ventricular inflow and to further calculate the time intervals required for right MPI calculation. Ninety pulsed-wave Doppler ultrasound images of the right ventricle in fetuses, forty-five showing outflow and forty-five inflow, were analyzed to automatically detect the valve clicks generated by tricuspid valve movement in inflow waves, and pulmonary valve movement in outflow waves. The morphological characteristics of waves were combined with intensity information to locate clicks. This automated method can detect valve movement events with a high positive predictive value (96.20-98.96%) and sensitivity (97.95-100.00%), using manual annotation from an expert ultrasonographer as the gold standard for evaluation.
机译:改良的心肌功能指数(Mod-MPI)引起了人们极大的兴趣,将其作为胎儿心脏功能评估的参数。但是,该指数的测量需要专业知识,并且由于其可重复性差,其临床应用可能受到限制。研究小组一直在研究左Mod-MPI(即左心室瓣事件产生的Mod-MPI),并且在我们先前的研究中已经开发出一种自动算法来计算左Mod-MPI。右MPI也很重要,因为在某些病理中它比左MPI更早变得异常。但是,要在整个胎龄范围内使用,都需要获取两张图像。本文提出了一种自动方法来检测房室流出和心室流入期间的瓣膜运动,并进一步计算正确的MPI计算所需的时间间隔。胎儿右心室的九十个脉冲多普勒超声图像进行了分析,其中四十五个显示了流出,四十五个流入,自动检测了流入波中三尖瓣运动和流出波中肺动脉瓣运动所产生的瓣膜滴答声。波的形态特征与强度信息相结合以定位咔嗒声。这种自动方法可以使用专家超声检查员的手动注释作为评估的金标准,以较高的阳性预测值(96.20-98.96%)和灵敏度(97.95-100.00%)检测瓣膜运动事件。

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