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Updated: Jun 25, 2025
Three-Dimensional Printing of a Complex Aortic Anomaly
Published on: November 1, 2018
Thoracic Aortic Three-Dimensional Geometry
Cameron Beeche1,2, Marie-Joe Dib2,3, Bingxin Zhao4
1Department of Bioengineering, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104.
Insights
Researchers developed an automated deep learning method to analyze the three-dimensional (3D) geometry of the aorta. This approach quantifies aortic structural parameters in large populations, aiding studies on aging and cardiovascular disease.
Area of Science:
- Cardiovascular Imaging
- Biomedical Engineering
- Radiomics
Background:
- Aortic structural degeneration, associated with aging, increases cardiovascular risks like left ventricular afterload and organ damage.
- Comprehensive characterization of three-dimensional (3D) aortic geometry in large populations remains limited.
- Understanding aortic structure is crucial for cardiovascular health assessment.
Purpose of the Study:
- To develop and deploy an automated deep learning method for comprehensive 3D thoracic aorta segmentation.
- To extract and analyze multiple aortic geometric phenotypes (AGPs) across diverse aortic subsegments.
- To enable large-scale investigations into the biological and clinical implications of aortic degeneration.
Main Methods:
- A deep learning architecture was utilized for complete thoracic aorta segmentation.
- Morphological image operations were employed to extract AGPs, including diameter, length, curvature, and tortuosity.
- The method was applied to imaging data from 54,241 UK Biobank participants and 8,456 Penn Medicine Biobank participants.
Main Results:
- A fully automated approach for quantifying 3D aortic structural parameters was established.
- Aortic geometric phenotypes were expanded across two large, representative biobanks.
- The study provides a scalable tool for analyzing aortic geometry in health and disease.
Conclusions:
- The developed automated method facilitates precise quantification of 3D aortic geometry.
- This approach enhances the available phenotypic data for large-scale cardiovascular research.
- It will support studies elucidating the mechanisms of aortic aging and disease.
Background:
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 towards 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.
