Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach

Minliang Liu, Liang Liang, Wei Sun

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The patient-specific biomechanical analysis of the aorta requires the quantification of the in vivo mechanical properties of individual patients. Current inverse approaches have attempted to estimate the nonlinear, anisotropic material parameters from in vivo image data using certain optimization schemes. However, since such inverse methods are dependent on iterative nonlinear optimization, these methods are highly computation-intensive. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate machine learning (ML) algorithms to expedite the procedure of in vivo material parameter identification. In this paper, we developed an ML-based approach to estimate the material parameters from three-dimensional aorta geometries obtained at two different blood pressure (i.e., systolic and diastolic) levels. The nonlinear relationship between the two loaded shapes and the constitutive parameters is established by an ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validations were used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.

Original languageEnglish (US)
Pages (from-to)201-217
Number of pages17
JournalComputer Methods in Applied Mechanics and Engineering
Volume347
DOIs
StatePublished - Apr 15 2019
Externally publishedYes

Keywords

  • Constitutive parameter estimation
  • Machine learning
  • Neural network

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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