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

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 sc...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2019-04, Vol.347, p.201-217
Main Authors: Liu, Minliang, Liang, Liang, Sun, Wei
Format: Article
Language:English
Subjects:
Algorithms
Aorta
Artificial intelligence
Biomechanical engineering
Biomechanics
Blood pressure
Computer simulation
Constitutive parameter estimation
Coronary vessels
Finite element method
In vivo methods and tests
Iterative methods
Machine learning
Mathematical models
Mechanical properties
Model accuracy
Neural network
Optimization
Parameter estimation
Parameter identification
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Summary: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.
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2018.12.030