Abstract：
BACKGROUND: Extracting phenotype-representative flow patterns and their associated numerical metrics is a bottleneck in the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) are a suitable strategy for deriving simple and interpretable clinical metrics of intraventricular flow suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients.
METHODS: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter grid search was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results.
RESULTS: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80 % of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1 % (HCM) to 13 % (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively.
CONCLUSIONS: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.
METHODS: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter grid search was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results.
RESULTS: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80 % of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1 % (HCM) to 13 % (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively.
CONCLUSIONS: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.
摘要：
背景：提取具有表型代表性的血流模式及其相关的数值指标是高级心脏血流成像模式临床转化的瓶颈。我们假设降阶模型（ROM）是得出简单且可解释的脑室内血流临床指标的合适策略，适用于进一步评估。结合基于机器学习（ML）流的ROM可以提供新的见解，以帮助诊断和风险分层患者。
方法：我们分析了81例非缺血性扩张型心肌病（DCM）患者的二维彩色多普勒超声心动图，51例肥厚型心肌病（HCM）患者，和77名正常志愿者（对照）。我们应用了适当的正交分解（POD）来构建患者特异性和队列特异性的LV流量ROM。每个ROM聚集少量的分量,表示通过时间相关系数沿着心动周期调制的空间相关速度图。我们使用故意简单的ML分析测试了三个分类器，这些ROM具有不同的监督级别。在监督模型中，超参数网格搜索用于推导最大化分类能力的ROM。分类器不了解LV室的几何形状和功能。我们在彩色多普勒序列上运行矢量流量映射，以帮助可视化流量模式并解释ML结果。
结果：基于POD的ROM通过10倍交叉验证稳定地代表了每个队列。主要POD模式在所有组群中捕获>80%的流动动能(KE),并且代表LV填充/排空射流。模式2代表舒张期涡旋，其KE贡献范围为<1％（HCM）至13％（DCM）。使用患者特异性ROM的半无监督分类显示，这两种主要模式的KE比率，涡流-射流(V2J)能量比，是一个简单的，区分DCM的可解释度量，HCM,控制患者。使用V2J作为分类器的接收器工作特征曲线的曲线下面积为0.81、0.91和0.95，用于区分HCM与Control,DCM与Control,和DCMvs.HCM,分别。
结论：心脏血流的模态分解可用于创建正常和病理性血流模式的ROM，发现简单的可解释的流量指标，具有区分疾病状态的能力，并且特别适用于使用ML的进一步处理。
方法：我们分析了81例非缺血性扩张型心肌病（DCM）患者的二维彩色多普勒超声心动图，51例肥厚型心肌病（HCM）患者，和77名正常志愿者（对照）。我们应用了适当的正交分解（POD）来构建患者特异性和队列特异性的LV流量ROM。每个ROM聚集少量的分量,表示通过时间相关系数沿着心动周期调制的空间相关速度图。我们使用故意简单的ML分析测试了三个分类器，这些ROM具有不同的监督级别。在监督模型中，超参数网格搜索用于推导最大化分类能力的ROM。分类器不了解LV室的几何形状和功能。我们在彩色多普勒序列上运行矢量流量映射，以帮助可视化流量模式并解释ML结果。
结果：基于POD的ROM通过10倍交叉验证稳定地代表了每个队列。主要POD模式在所有组群中捕获>80%的流动动能(KE),并且代表LV填充/排空射流。模式2代表舒张期涡旋，其KE贡献范围为<1％（HCM）至13％（DCM）。使用患者特异性ROM的半无监督分类显示，这两种主要模式的KE比率，涡流-射流(V2J)能量比，是一个简单的，区分DCM的可解释度量，HCM,控制患者。使用V2J作为分类器的接收器工作特征曲线的曲线下面积为0.81、0.91和0.95，用于区分HCM与Control,DCM与Control,和DCMvs.HCM,分别。
结论：心脏血流的模态分解可用于创建正常和病理性血流模式的ROM，发现简单的可解释的流量指标，具有区分疾病状态的能力，并且特别适用于使用ML的进一步处理。
