Menon Prahlad G, Morris Lailonny, Staines Mara, Lima Joao, Lee Daniel C, Gopalakrishnan Vanathi
Department of Biomedical Informatics, University of Pittsburgh, USA ; Electrical & Computer Engineering, Sun Yat-Sen University - Carnegie Mellon University Joint Institute of Engineering, USA.
Department of Biomedical Informatics, University of Pittsburgh, USA.
Proc SPIE Int Soc Opt Eng. 2014 Feb 15;9034. doi: 10.1117/12.2042118.
Characterization of regional left ventricular (LV) function may have application in prognosticating timely response and informing choice therapy in patients with ischemic cardiomyopathy. The purpose of this study is to characterize LV function through a systematic analysis of 4D (3D + time) endocardial motion over the cardiac cycle in an effort to define objective, clinically useful metrics of pathological remodeling and declining cardiac performance, using standard cardiac MRI data for two distinct patient cohorts accessed from CardiacAtlas.org: a) MESA - a cohort of asymptomatic patients; and b) DETERMINE - a cohort of symptomatic patients with a history of ischemic heart disease (IHD) or myocardial infarction. The LV endocardium was segmented and a signed phase-to-phase Hausdorff distance (HD) was computed at 3D uniformly spaced points tracked on segmented endocardial surface contours, over the cardiac cycle. An LV-averaged index of phase-to-phase endocardial displacement (P2PD) time-histories was computed at each tracked point, using the HD computed between consecutive cardiac phases. Average and standard deviation in P2PD over the cardiac cycle was used to prepare characteristic curves for the asymptomatic and IHD cohort. A novel biomarker of RMS error between mean patient-specific characteristic P2PD over the cardiac cycle for each individual patient and the cumulative P2PD characteristic of a cohort of asymptomatic patients was established as the RMS-P2PD marker. The novel RMS-P2PD marker was tested as a cardiac function based feature for automatic patient classification using a Bayesian Rule Learning (BRL) framework. The RMS-P2PD biomarker indices were significantly different for the symptomatic patient and asymptomatic control cohorts (p<0.001). BRL accurately classified 83.8% of patients correctly from the patient and control populations, with leave-one-out cross validation, using standard indices of LV ejection fraction (LV-EF) and LV end-systolic volume index (LV-ESVI). This improved to 91.9% with inclusion of the RMS-P2PD biomarker and was congruent with improvements in both sensitivity for classifying patients and specificity for identifying asymptomatic controls from 82.6% up to 95.7%. RMS-P2PD, when contrasted against a collective normal reference, is a promising biomarker to investigate further in its utility for identifying quantitative signs of pathological endocardial function which may boost standard image makers as precursors of declining cardiac performance.
区域左心室（LV）功能的特征描述可能有助于预测缺血性心肌病患者的及时反应并指导治疗选择。本研究的目的是通过系统分析心动周期中的4D（3D+时间）心内膜运动来表征左心室功能，以定义病理性重塑和心脏功能下降的客观、临床有用指标，使用从CardiacAtlas.org获取的两个不同患者队列的标准心脏MRI数据：a）MESA——一组无症状患者；b）DETERMINE——一组有缺血性心脏病（IHD）或心肌梗死病史的有症状患者。对左心室心内膜进行分割，并在心动周期内在分割的心内膜表面轮廓上跟踪的3D均匀间隔点处计算有符号的逐相豪斯多夫距离（HD）。在每个跟踪点计算逐相心内膜位移（P2PD）时间历程的左心室平均指数，使用连续心动周期之间计算的HD。心动周期内P2PD的平均值和标准差用于为无症状和IHD队列绘制特征曲线。建立了一种新的生物标志物，即每个个体患者心动周期内平均患者特异性特征P2PD与无症状患者队列累积P2PD特征之间的均方根误差（RMS），作为RMS-P2PD标志物。使用贝叶斯规则学习（BRL）框架，将新的RMS-P2PD标志物作为基于心脏功能的特征进行自动患者分类测试。有症状患者和无症状对照队列的RMS-P2PD生物标志物指数有显著差异（p<0.001）。使用左心室射血分数（LV-EF）和左心室收缩末期容积指数（LV-ESVI）的标准指数，通过留一法交叉验证，BRL能正确分类83.8%的患者。加入RMS-P2PD生物标志物后，这一比例提高到91.9%，并且在对患者分类的敏感性和识别无症状对照的特异性方面都有所提高，从82.6%提高到95.7%。与集体正常参考相比，RMS-P2PD是一种很有前景的生物标志物，可进一步研究其在识别病理性心内膜功能定量体征方面的效用，这可能会增强标准图像指标作为心脏功能下降先兆的作用。
