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Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest

机译：静态计算机断层扫描血管造影图像中的心肌灌注分析

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Cardiac computed tomography angiography (CTA) is a non-invasive method for anatomic evaluation of coronary artery stenoses. However, CTA is prone to artifacts that reduce the diagnostic accuracy to identify stenoses. Further, CTA does not allow for determination of the physiologic significance of the visualized stenoses. In this paper, we propose a new system to determine the physiologic manifestation of coronary stenoses by assessment of myocardial perfusion from typically acquired CTA images at rest. As a first step, we develop an automated segmentation method to delineate the left ventricle. Both endocardium and epicardium are compactly modeled with subdivision surfaces and coupled by explicit thickness representation. After initialization with five anatomical landmarks, the model is adapted to a target image by deformation increments including control vertex displacements and thickness variations guided by trained AdaBoost classifiers, and regularized by a prior of deformation increments from principal component analysis (PCA). The evaluation using a 5-fold cross-validation demonstrates the overall segmentation error to be 1.00 +/- 0.39 mm for endocardium and 1.06 +/- 0.43 mm for epicardium, with a boundary contour alignment error of 2.79 +/- 0.52. Based on our LV model, two types of myocardial perfusion analyzes have been performed. One is a perfusion network analysis, which explores the correlation (as network edges) pattern of perfusion between all pairs of myocardial segments (as network nodes) defined in AHA 17-segment model. We find perfusion network display different patterns in the normal and disease groups, as divided by whether significant coronary stenosis is present in quantitative coronary angiography (QCA). The other analysis is a clinical validation assessment of the ability of the developed algorithm to predict whether a patient has significant coronary stenosis when referenced to an invasive QCA ground truth standard. By training three machine learning techniques using three features of normalized perfusion intensity, transmural perfusion ratio, and myocardial wall thickness, we demonstrate AdaBoost to be slightly better than Naive Bayes and Random Forest by the area under receiver operating characteristics (ROC) curve. For the AdaBoost algorithm, an optimal cut-point reveals an accuracy of 0.70, with sensitivity and specificity of 0.79 and 0.64, respectively. Our study shows perfusion analysis from CTA images acquired at rest is useful for providing physiologic information in diagnosis of obstructive coronary artery stenoses. (C) 2015 Elsevier B.V. All rights reserved.
机译：心脏计算机断层造影血管造影（CTA）是一种用于冠状动脉狭窄的解剖评估的非侵入性方法。但是，CTA容易出现伪影，从而降低了诊断诊断狭窄的准确性。此外，CTA不允许确定可视化狭窄的生理学意义。在本文中，我们提出了一种新的系统，该系统可通过从静止时通常获得的CTA图像评估心肌灌注来确定冠状动脉狭窄的生理表现。第一步，我们开发一种自动分割方法来描绘左心室。心内膜和心外膜均使用细分表面进行紧凑建模，并通过显式厚度表示进行耦合。用五个解剖学界标初始化后，该模型通过变形增量（包括受控制的AdaBoost分类器引导的控制顶点位移和厚度变化）适应目标图像，并通过来自主成分分析（PCA）的变形增量先验进行正则化。使用5倍交叉验证的评估表明，对于心内膜，整体分割误差为1.00 +/- 0.39毫米，对于心外膜为1.06 +/- 0.43毫米，边界轮廓对准误差为2.79 +/- 0.52。基于我们的LV模型，已经进行了两种类型的心肌灌注分析。一种是灌注网络分析，它探讨了在AHA 17段模型中定义的所有成对的心肌段（作为网络节点）之间灌注的相关性（作为网络边缘）模式。我们发现，在正常和疾病组中，灌注网络显示出不同的模式，除以定量冠状动脉造影（QCA）中是否存在明显的冠状动脉狭窄。另一项分析是对所开发算法在参考侵入性QCA地面真相标准时预测患者是否患有严重冠状动脉狭窄的能力的临床验证评估。通过使用归一化灌注强度，透壁灌注率和心肌壁厚度的三个特征训练三种机器学习技术，我们证明了AdaBoost在接收器操作特征（ROC）曲线下的面积略优于朴素贝叶斯和随机森林。对于AdaBoost算法，最佳切点显示的准确度为0.70，灵敏度和特异性分别为0.79和0.64。我们的研究表明，静止时获取的CTA图像的灌注分析可为诊断梗阻性冠状动脉狭窄提供生理信息。 （C）2015 Elsevier B.V.保留所有权利。

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