Ventricular structure in ARVC: going beyond volumes as a measure of risk
Ventricular structure in ARVC: going beyond volumes as a measure of risk
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DOI：
10.1186/s12968-016-0291-9
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发表时间：
2016-10-14
影响因子：
6.4
通讯作者：
Haugaa, Kristina Hermann
中科院分区：
文献类型：
--
作者：
McLeod, Kristin;Wall, Samuel;Haugaa, Kristina Hermann
背景资料：右室结构改变是致心律失常性右室心肌病（ARVC）的重要特征，但客观量化具有挑战性。本研究的目的是超越心室容积和直径，并探索是否可以评估右心室和左心室的形状并与临床测量相关。我们使用可量化的计算方法来自动识别和分析ARVC患者心血管磁共振（CMR）图像中的畸形。此外，我们研究了如何自动提取的结构特征相关的mammic events.Methods：回顾性横断面可行性研究进行CMR短轴电影图像的27例ARVC患者和21名老年无症状对照组。在心动周期的舒张末期（艾德）和收缩末期（ES）阶段对所有图像进行分割，以创建每个受试者的三维（3D）双心室形状模型。确定了ARVC人群中单心室和双心室形状的最常见组成部分，并与对照组进行了比较。除了临床结果，如室性心律失常，还计算了2010年工作组标准中确定的ARVC形状和参数之间的相关性。结果：ARVC人群的双心室形状显示，按患病率排序，每个形状解释了人群总方差的百分比：两个心室的整体扩张/收缩（44%），右心室（RV）流出道的延长/缩短（15%），隔膜的倾斜（10%），两个心室的缩短/延长（7%），右心室流入道和流出道的膨出/缩短（5%）。双心室形状与几个临床诊断参数和结局显著相关，包括（但不限于）总体扩张与心电图（ECG）主要标准（p = 0.002）之间的相关性，以及基底至心尖延长与心律失常病史（p = 0.003）之间的相关性。使用形状模式的ARVC与对照的分类产生了高灵敏度（96%）和中度特异性（81%）。结论：我们首次提出了一种自动方法，用于量化和分析ARVC患者的心室形状CMR图像。特定的心室形状特征与ARVC患者的诊断指标高度相关，并产生较高的分类敏感性。心室形状分析可能是一种新的方法来分类ARVC疾病，并可用于室性心律失常的诊断和危险分层。
Background: Altered right ventricular structure is an important feature of Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), but is challenging to quantify objectively. The aim of this study was to go beyond ventricular volumes and diameters and to explore if the shape of the right and left ventricles could be assessed and related to clinical measures. We used quantifiable computational methods to automatically identify and analyse malformations in ARVC patients from Cardiovascular Magnetic Resonance (CMR) images. Furthermore, we investigated how automatically extracted structural features were related to arrhythmic events.Methods: A retrospective cross-sectional feasibility study was performed on CMR short axis cine images of 27 ARVC patients and 21 ageing asymptomatic control subjects. All images were segmented at the end-diastolic (ED) and end-systolic (ES) phases of the cardiac cycle to create three-dimensional (3D) bi-ventricle shape models for each subject. The most common components to single-and bi-ventricular shape in the ARVC population were identified and compared to those obtained from the control group. The correlations were calculated between identified ARVC shapes and parameters from the 2010 Task Force Criteria, in addition to clinical outcomes such as ventricular arrhythmias.Results: Bi-ventricle shape for the ARVC population showed, as ordered by prevalence with the percent of total variance in the population explained by each shape: global dilation/shrinking of both ventricles (44 %), elongation/shortening at the right ventricle (RV) outflow tract (15 %), tilting at the septum (10 %), shortening/lengthening of both ventricles (7 %), and bulging/shortening at both the RV inflow and outflow (5 %). Bi-ventricle shapes were significantly correlated to several clinical diagnostic parameters and outcomes, including (but not limited to) correlations between global dilation and electrocardiography (ECG) major criteria (p = 0.002), and base-to-apex lengthening and history of arrhythmias (p = 0.003). Classification of ARVC vs. control using shape modes yielded high sensitivity (96 %) and moderate specificity (81 %).Conclusion: We presented for the first time an automatic method for quantifying and analysing ventricular shapes in ARVC patients from CMR images. Specific ventricular shape features were highly correlated with diagnostic indices in ARVC patients and yielded high classification sensitivity. Ventricular shape analysis may be a novel approach to classify ARVC disease, and may be used in diagnosis and in risk stratification for ventricular arrhythmias.