整合健康人类心脏的解剖学与电生理学：基于通用坐标的双心室统计形状分析见解
Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates
AI总结：
本研究开发了双心室统计形状模型，利用271名健康个体的CT数据量化双心室解剖结构对心电图及人口特征的影响，并提供适合电生理模拟的合成网格，为心脏电生理数字孪生发展提供新见解。
摘要：
心脏数字孪生是患者特异性心脏的虚拟副本，模拟其解剖结构与生理功能。构建心脏数字孪生的关键步骤是解剖学孪生，即调整数字孪生的计算网格以适配患者特异性的心脏解剖结构。许多研究利用计算模拟，调查了解剖学变异对心电图（ECG）等临床相关功能测量的影响。尽管此类模拟环境为研究人员提供了精心控制的真实基准，但解剖学差异对真实世界患者功能测量的影响仍未得到充分研究。在本研究中，我们开发了一个双心室统计形状模型，并利用它来量化双心室解剖结构对ECG衍生特征和人口统计学特征的影响，为心脏电生理学数字孪生的发展提供新见解。为此，利用了一个包含来自271名健康个体（包括运动员）的高分辨率心脏CT扫描的数据集。此外，还开发了一种基于通用心室坐标的新颖方法，以建立轻量级的形状对应关系。该形状模型的性能得到了严格验证，重点关注其降维能力和训练数据需求。此外，还提供了一个综合的合成队列，包含具有纤维结构和解剖区域注释的即用型双心室网格。这些网格非常适合用于电生理模拟。
英文摘要：
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to the patient-specific cardiac anatomy. In a number of studies, the effect of anatomical variation on clinically relevant functional measurements like electrocardiograms (ECGs) is investigated, using computational simulations. While such a simulation environment provides researchers with a carefully controlled ground truth, the impact of anatomical differences on functional measurements in real-world patients remains understudied. In this study, we develop a biventricular statistical shape model and use it to quantify the effect of biventricular anatomy on ECG-derived and demographic features, providing novel insights for the development of digital twins of cardiac electrophysiology. To this end, a dataset comprising high-resolution cardiac CT scans from 271 healthy individuals, including athletes, is utilized. Furthermore, a novel, universal, ventricular coordinate-based method is developed to establish lightweight shape correspondence. The performance of the shape model is rigorously established, focusing on its dimensionality reduction capabilities and the training data requirements. Additionally, a comprehensive synthetic cohort is made available, featuring ready-to-use biventricular meshes with fiber structures and anatomical region annotations. These meshes are well-suited for electrophysiological simulations.
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