Thoracic Aortic Three-Dimensional Geometry.

Cameron Beeche,Marie-Joe Dib,Bingxin Zhao,Joe David Azzo, Hamed Tavolinejad,Hannah Maynard,Jeffrey Thomas Duda,James Gee,Oday Salman,Penn Medicine BioBank,Walter R Witschey,Julio Chirinos

Pulse (Basel, Switzerland)（2025）

Department of Bioengineering | Division of Cardiovascular Medicine | Department of Statistics and Data Science | University of Pennsylvania Perelman School of Medicine | Department of Radiology

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Abstract

Introduction:Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations. Methods:We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank. Conclusion:Our method provides a fully automated approach toward quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.

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【要点】：本文利用卷积神经网络技术，首次全面评估了三维主动脉几何形态对心血管健康的影响，并发现了与之相关的遗传结构。

【方法】：通过使用U-Net卷积神经网络和形态学操作，对UK Biobank的53,612名参与者和Penn Medicine Biobank的8,066名参与者的主动脉3D几何形态进行了量化。

【实验】：在两个大型生物库中进行了实验，使用的数据集为UK Biobank和Penn Medicine Biobank，实验结果显示，主动脉结构退化的3D几何形态与高血压和心脏病有显著相关性，并能预测新发高血压、心力衰竭、心肌病和心房颤动。此外，还发现了与3D主动脉几何形态相关的237个新型遗传位点。