Thoracic Aortic Three-Dimensional Geometry.

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.