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Updated: May 13, 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, Philadelphia, PA, USA.
Insights
This study introduces a deep learning method to analyze the three-dimensional (3D) aortic geometry in large populations. This approach quantifies aortic structural parameters, aiding research into aging and cardiovascular disease.
Area of Science:
- Cardiovascular Imaging
- Biomedical Engineering
- Radiology
Background:
- Aortic structural degeneration, linked to aging, increases cardiovascular risks by affecting left ventricular afterload and arterial pulsatility.
- Comprehensive characterization of three-dimensional (3D) aortic geometry in large populations is lacking.
- 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 multiple aortic geometric phenotypes (AGPs) across diverse aortic subsegments.
- To enable large-scale studies on the clinical implications of aortic structural changes.
Main Methods:
- A deep learning architecture was utilized for complete thoracic aorta segmentation.
- Morphological image operations were employed to derive 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 successfully segmented the complete thoracic aorta in a large cohort.
Conclusions:
- The developed automated method facilitates the quantification of 3D aortic geometry.
- This approach enhances the available phenotypic data for large-scale cardiovascular research.
- It will aid in elucidating the biology and clinical consequences of aortic degeneration in aging and disease.
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.
