Automated Quantification of Diseased Thoracic Aortic Longitudinal Centerline and Surface Curvatures
Journal article, 2020

Precise description of vascular morphometry is crucial to support medical device manufacturers and clinicians for improving device development and interventional outcomes. A compact and intuitive method is presented to automatically characterize the surface geometry of tubular anatomic structures and quantify surface curvatures starting from generic stereolithographic (STL) surfaces. The method was validated with software phantoms and used to quantify the longitudinal surface curvatures of 37 human thoracic aortas with aneurysm or dissection. The quantification of surface curvatures showed good agreement with analytic solutions from the software phantoms, and demonstrated better agreement as compared to estimation methods using only centerline geometry and cross-sectional radii. For the human thoracic aortas, longitudinal inner surface curvature was significantly higher than centerline curvature (0.33 +/- 0.06 versus 0.16 +/- 0.02cm(-1) for mean; 1.38 +/- 0.48 versus 0.45 +/- 0.11cm(-1) for peak; both p<0.001). These findings show the importance of quantifying surface curvatures in order to better describe the geometry and biomechanical behavior of the thoracic aorta, which can assist in treatment planning and supplying device manufactures with more precise boundary conditions for mechanical evaluation.

geometric modeling

Thoracic aorta

dissection

aneurysm

surface curvature

Author

Johan Bondesson

Chalmers, Mechanics and Maritime Sciences, Dynamics

Ga-Young Suh

Stanford University

Torbjörn Lundh

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Jason T. Lee

Stanford University

Michael D. Dake

Stanford University

Christopher P. Cheng

Stanford University

Journal of Biomechanical Engineering

0148-0731 (ISSN) 1528-8951 (eISSN)

Vol. 142 4 041007

Subject Categories

Other Medical Engineering

Biophysics

Other Materials Engineering

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1115/1.4045271

PubMed

31633168

More information

Latest update

6/18/2020