On the role of material properties in ascending thoracic aortic aneurysms

Massimiliano Zingales, Federica Cosentino, Valentina Agnese, Salvatore Pasta, Michele Pilato, Diego Bellavia, Giuseppe M. Raffa, Giovanni Gentile, Salvatore Pasta, Valentina Agnese, Federica Cosentino, Giovanni Domenico Gentile

Risultato della ricerca: Articlepeer review

5 Citazioni (Scopus)

Abstract

One of the obstacles standing before the biomechanical analysis of an ascending thoracic aortic aneurysm (ATAA) is the difficulty in obtaining patient-specific material properties. This study aimed to evaluate differences on ATAA-related stress predictions resulting from the elastostatic analysis based on the optimization of arbitrary material properties versus the application of patient-specific material properties determined from ex-vivo biaxial testing. Specifically, the elastostatic analysis relies the on the fact that, if the aortic wall stress does not depend on material properties, the aorta has to be statistically determinate. Finite element analysis (FEA) was applied to a group of nine patients who underwent both angio-CT imaging to reconstruct ATAA anatomies and surgical repair of diseased aorta to collect tissue samples for experimental material testing. Tissue samples cut from excised ATAA rings were tested under equibiaxial loading conditions to obtain experimentally-derived material parameters by fitting stress-strain profiles. FEAs were carried out using both optimized and experimentally-derived material parameters to predict and compare the stress distribution using the mean absolute percentage error (MAPE). Although physiological strains were below yield point (range of 0.08-0.25), elastostatic analysis led to errors on the stress predictions that depended on the type of constitutive model (highest MAPE of 0.7545 for Yeoh model and 0.7683 for Fung model) and ATAA geometry (lowest MAPE of 0.0349 for patient P.7). Elastostatic analysis needs better understanding of its application for determining aneurysm mechanics, and patient-specific material parameters are essential for reliable accurate stress predictions in ATAAs.
Lingua originaleEnglish
pagine (da-a)70-78
Numero di pagine9
RivistaComputers in Biology and Medicine
Volume109
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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