Hiremath Pranoti, Bauer Michael, Cheng Hui-Wen, Unno Kazumasa, Liao Ronglih, Cheng Susan
Harvard Medical School.
J Vis Exp. 2014 Jan 14(83):e50850. doi: 10.3791/50850.
Echocardiography is a widely accessible imaging modality that is commonly used to noninvasively characterize and quantify changes in cardiac structure and function. Ultrasonic assessments of cardiac tissue can include analyses of backscatter signal intensity within a given region of interest. Previously established techniques have relied predominantly on the integrated or mean value of backscatter signal intensities, which may be susceptible to variability from aliased data from low frame rates and time delays for algorithms based on cyclic variation. Herein, we describe an ultrasound-based imaging algorithm that extends from previous methods, can be applied to a single image frame and accounts for the full distribution of signal intensity values derived from a given myocardial sample. When applied to representative mouse and human imaging data, the algorithm distinguishes between subjects with and without exposure to chronic afterload resistance. The algorithm offers an enhanced surrogate measure of myocardial microstructure and can be performed using open-access image analysis software.
超声心动图是一种广泛可用的成像方式,常用于以非侵入性方式表征和量化心脏结构和功能的变化。对心脏组织的超声评估可包括对给定感兴趣区域内背散射信号强度的分析。先前建立的技术主要依赖于背散射信号强度的积分或平均值,这可能容易受到基于循环变化的算法因低帧率和时间延迟导致的混叠数据的变异性影响。在此,我们描述一种基于超声的成像算法,该算法在先前方法的基础上进行了扩展,可应用于单个图像帧,并考虑了来自给定心肌样本的信号强度值的完整分布。当应用于代表性的小鼠和人类成像数据时,该算法能够区分有或没有暴露于慢性后负荷抵抗的受试者。该算法提供了一种增强的心肌微观结构替代测量方法,并且可以使用开放获取的图像分析软件来执行。