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形态标准化的左心室运动指标源于 MRI 特征追踪,可用于表征心肌梗死。

Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial infarction.

机构信息

Dipartimento di Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Via del Policlinico 155, 00186, Roma, Italy.

Dipartimento di Ingegneria Strutturale e Geotecnica, Sapienza Università di Roma, Via Eudossiana 18, 00184, Roma, Italy.

出版信息

Sci Rep. 2017 Sep 25;7(1):12259. doi: 10.1038/s41598-017-12539-5.

DOI:10.1038/s41598-017-12539-5
PMID:28947754
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5612925/
Abstract

We characterized motion attributes arising from LV spatio-temporal analysis of motion distributions in myocardial infarction. Time-varying 3D finite element shape models were obtained in 300 Controls and 300 patients with myocardial infarction. Inter-individual left ventricular shape differences were eliminated using parallel transport to the grand mean of all cases. The first three principal component (PC) scores were used to characterize trajectory attributes. Scores were tested with ANOVA/MANOVA using patient disease status (Infarcts vs. Controls) as a factor. Infarcted patients had significantly different magnitude, orientation and shape of left ventricular trajectories in comparison to Controls. Significant differences were found for the angle between PC scores 1 and 2 in the endocardium, and PC scores 1 and 3 in the epicardium. The largest differences were found in the magnitude of endocardial motion. Endocardial PC scores in shape space showed the highest classification power using support vector machine, with higher total accuracy in comparison to previous methods. Shape space performed better than size-and-shape space for both epicardial and endocardial features. In conclusion, LV spatio-temporal motion attributes accurately characterize the presence of infarction. This approach is easily generalizable to different pathologies, enabling more precise study of the pathophysiological consequences of a wide spectrum of cardiac diseases.

摘要

我们对心肌梗死患者的左心室时空运动分布进行了运动特征分析,以明确运动属性。我们在 300 例对照者和 300 例心肌梗死患者中获得了时变的 3D 有限元形态模型。采用平行传输方法将个体间左心室形态差异校正到所有病例的平均值。采用主成分分析(PCA)的前三个得分来描述轨迹特征。采用单因素方差分析(ANOVA)/多因素方差分析(MANOVA)对患者疾病状态(梗死与对照)进行检验。与对照者相比,梗死患者的左心室运动轨迹在幅度、方向和形状上均有显著差异。在心肌内层,PC 得分 1 和 2 之间以及心外膜 PC 得分 1 和 3 之间的夹角存在显著差异。心内膜运动幅度的差异最大。心内膜在形状空间中的 PCA 得分在支持向量机中表现出最高的分类能力,与以前的方法相比,其总准确率更高。与大小和形状空间相比,形状空间在心肌内层和心外膜特征上均表现出更好的性能。总之,左心室时空运动属性能够准确地描述梗死的存在。这种方法很容易推广到不同的病理类型,能够更精确地研究广泛的心脏疾病的病理生理后果。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bae2/5612925/545701149433/41598_2017_12539_Fig8_HTML.jpg
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