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首页> 外文期刊>Annals of Biomedical Engineering: The Journal of the Biomedical Engineering Society >Measuring regional changes in the diastolic deformation of the left ventricle of SHR rats using microPET technology and hyperelastic warping.
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Measuring regional changes in the diastolic deformation of the left ventricle of SHR rats using microPET technology and hyperelastic warping.

机译:使用microPET技术和超弹性翘曲测量SHR大鼠左心室舒张变形的区域变化。

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The objective of this research was to assess applicability of a technique known as hyperelastic warping for the measurement of local strains in the left ventricle (LV) directly from microPET image data sets. The technique uses differences in image intensities between template (reference) and target (loaded) image data sets to generate a body force that deforms a finite element (FE) representation of the template so that it registers with the target images. For validation, the template image was defined as the end-systolic microPET image data set from a Wistar Kyoto (WKY) rat. The target image was created by mapping the template image using the deformation results obtained from a FE model of diastolic filling. Regression analysis revealed highly significant correlations between the simulated forward FE solution and image derived warping predictions for fiber stretch (R (2) = 0.96), circumferential strain (R (2) = 0.96), radial strain (R (2) = 0.93), and longitudinal strain (R (2) = 0.76) (p 0.001 for all cases). The technology was applied to microPET image data of two spontaneously hypertensive rats (SHR) and a WKY control. Regional analysis revealed that, the lateral freewall in the SHR subjects showed the greatest deformation compared with the other wall segments. This work indicates that warping can accurately predict the strain distributions during diastole from the analysis of microPET data sets.
机译:这项研究的目的是直接从microPET图像数据集评估一种称为超弹性翘曲的技术在测量左心室(LV)局部应变方面的适用性。该技术使用模板(参考)和目标(已加载)图像数据集之间的图像强度差异来生成使模板的有限元(FE)表示变形的体力,使其与目标图像对齐。为了验证,将模板图像定义为来自Wistar Kyoto(WKY)大鼠的收缩末期microPET图像数据集。通过使用从舒张期充盈的FE模型获得的变形结果映射模板图像来创建目标图像。回归分析显示,模拟的正向有限元求解与图像得出的纤维伸长率(R(2)= 0.96),周向应变(R(2)= 0.96),径向应变(R(2)= 0.93)的翘曲预测之间具有高度显着的相关性。 ,以及纵向应变(R(2)= 0.76)(所有情况下p <0.001)。该技术已应用于两只自发性高血压大鼠(SHR)和WKY对照的microPET图像数据。区域分析表明,与其他墙段相比,SHR受试者的侧向自由墙变形最大。这项工作表明,翘曲可以通过对microPET数据集的分析来准确预测舒张期的应变分布。

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