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Related Concept Videos

Thoracic Aorta01:15

Thoracic Aorta

281
The thoracic section of the aorta begins at the T5 vertebra and extends to the T12 level at the diaphragm, initially progressing through the mediastinum to the left of the spinal column. Throughout its course in the thoracic segment, the thoracic aorta emits various offshoots known collectively as visceral and parietal branches. The branches that predominantly supply blood to visceral organs are termed visceral branches and include bronchial, pericardial, esophageal, and mediastinal arteries,...
281

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Related Experiment Video

Updated: May 13, 2025

Three-Dimensional Printing of a Complex Aortic Anomaly
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Published on: November 1, 2018

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Thoracic Aortic Three-Dimensional Geometry.

Cameron Beeche1,2, Marie-Joe Dib2,3, Bingxin Zhao4

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.

Pulse (Basel, Switzerland)
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
3D aortic structureAutomated segmentationDeep learningThoracic aorta

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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.