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Automatic assessment of regional and global wall motion abnormalities in echocardiography images by nonlinear dimensionality reduction.

机译:通过非线性降维自动评估超声心动图图像中区域和整体壁运动异常。

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Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three-point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia.Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two-dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three-point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients.Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82% and a relative agreement of 97%.The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three-point scale in clinical evaluations.
机译:鉴定和评估左心室(LV)整体和区域壁运动(RWM)异常对于各种心血管疾病的临床评估至关重要。目前,这种评估是在视觉上进行的,这在很大程度上取决于超声心动图医师的培训和经验,因此易于在观察者之间和观察者内部产生很大的差异。本文提出了一种新的自动方法,该方法基于非线性降维(NLDR)进行整体壁运动评估,并以三点尺度对左室壁的RWM异常进行检测和分类,方法如下:(1)运动正常,(2)运动不足(3)运动障碍等距特征映射(Isomap)是最流行的NLDR算法之一。在本文中,将Isomap算法的修改版本(其中使用非刚性配准计算图像到图像的距离度量)应用于心脏一个周期的二维(2D)超声心动图图像。通过这种方法,这些图像中的非线性信息被嵌入2D流形中,每个图像的特征都在于所构建的流形上的一个点。这种新的表示方式根据LV体积变化可视化了这些图像之间的关系。然后,从合成的流形中提出了新的全局和区域定量指标,用于全局壁运动估计以及三点量表中LV壁的RWM分类。将我们的方法获得的结果定量评估为由两名经验丰富的超声心动图医师以视觉方式获得的结果作为参考(金标准),对10名健康志愿者和14位患者进行了线性回归分析。由经验丰富的超声心动图医师获得的左室射血分数得出的相关系数分别为0.85和0.90。拟议的RWM评分与参考视觉评分之间的比较得出了82%的绝对一致性和97%的相对一致性。该诊断方法可作为有用的工具以及经验丰富的超声心动图医师的参考视觉评估在临床评估中以三点量表进行整体壁运动估计以及左室壁RWM异常分类。

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