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[Preprint]. 2024 Oct 31:2024.05.09.593413.
doi: 10.1101/2024.05.09.593413.

Thoracic Aortic Three-Dimensional Geometry

Cameron Beeche  1   2 , Marie-Joe Dib  2   3 , Bingxin Zhao  4 , Joe David Azzo  3 , Hamed Tavolinejad  2   4 , Hannah Maynard  2 , Jeffrey Duda  5 , James Gee  5 , Oday Salman  3 ; Penn Medicine BioBank; Walter R Witschey  5 , Julio A Chirinos  2   3
Affiliations

Thoracic Aortic Three-Dimensional Geometry

Cameron Beeche et al. bioRxiv. .

Update in

  • Thoracic Aortic Three-Dimensional Geometry.
    Beeche C, Dib MJ, Zhao B, Azzo JD, Tavolinejad H, Maynard H, Duda JT, Gee J, Salman O; Penn Medicine BioBank; Witschey WR, Chirinos J. Beeche C, et al. Pulse (Basel). 2025 Jan 27;13(1):72-79. doi: 10.1159/000543613. eCollection 2025 Jan-Dec. Pulse (Basel). 2025. PMID: 40330437

Abstract

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.

Keywords: 3D aortic structure; Deep learning; UK Biobank.

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Conflict of interest statement

(1) Dr. Chirinos is supported by NIH grants U01-TR003734, U01-TR003734-01S1, UO1-HL160277, R33-HL-146390, R01-HL153646, K24-AG070459, R01-AG058969, R01-HL157108, R01-HL155599, R01-HL104106 and R01HL155764. He has recently consulted for Bayer, Fukuda-Denshi, Bristol-Myers Squibb, Biohaven Pharmaceuticals, Johnson & Johnson, Edwards Life Sciences, Merck, and NGM Biopharmaceuticals. He received University of Pennsylvania research grants from National Institutes of Health, Fukuda-Denshi, Bristol-Myers Squibb, Microsoft and Abbott. He is named as inventor in a University of Pennsylvania patent for the use of inorganic nitrates/nitrites for the treatment of Heart Failure and Preserved Ejection Fraction and for the use of biomarkers in heart failure with preserved ejection fraction. He has received payments for editorial roles from the American Heart Association, the American College of Cardiology, Elsevier and Wiley, and payments for academic roles from the University of Texas, Boston University, and Virginia Commonwealth University. He has received research device loans from Atcor Medical, Fukuda-Denshi, Unex, Uscom, NDD Medical Technologies, Microsoft and MicroVision Medical. (2) The remaining authors have nothing to disclose.

Figures

Fig. 1.
Fig. 1.
Overview of the U-Net segmentation architecture for performing segmentation of axial MRI images in the UK Biobank.
Fig. 2.
Fig. 2.
(A) Aortic mesh legend for each AGP region. (B) Visualization of three-dimensional aortic mesh.
See this image and copyright information in PMC

References

    1. Chirinos J.A., Segers P., Hughes T. & Townsend R. Large-Artery Stiffness in Health and Disease: JACC State-of-the-Art Review. Journal of the American College of Cardiology 74, 1237–1263 (2019). - PMC - PubMed
    1. Vasan R.S., Song R.J., Xanthakis V. & Mitchell G.F. Aortic Root Diameter and Arterial Stiffness: Conjoint Relations to the Incidence of Cardiovascular Disease in the Framingham Heart Study. Hypertension 78, 1278–1286 (2021). - PMC - PubMed
    1. Bouke P.A. et al. Aortic elongation part I: the normal aortic ageing process. Heart 104, 1772 (2018). - PubMed
    1. Samuel H. et al. Aortic elongation part II: the risk of acute type A aortic dissection. Heart 104, 1778 (2018). - PubMed
    1. Bai W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. Journal of Cardiovascular Magnetic Resonance 20, 65 (2018). - PMC - PubMed

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