Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

Minliang Liu, Liang Liang, Wei Sun
doi: https://doi.org/10.1101/366963
Minliang Liu
1Tissue Mechanics Laboratory, Atlanta, GA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Liang Liang
2The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wei Sun
3Georgia Institute of Technology and Emory University, Atlanta, GA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

The patient-specific biomechanical analysis of the aorta demands the in vivo mechanical properties of individual patients. Current inverse approaches have shown the feasibility of estimating 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, which may take weeks to complete for only a single patient, inhibiting rapid feedback for clinical use. Recently, machine learning (ML) techniques have led to revolutionary breakthroughs in many applications. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate ML algorithms to expedite the procedure of in vivo material parameter identification. In this paper, we developed a ML-based approach to identify the material parameters from three-dimensional aorta geometries obtained at two different blood pressure levels, namely systolic and diastolic geometries. The nonlinear relationship between the two loaded shapes and the constitutive parameters are established by a ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validation was used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 10, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Estimation of in vivo constitutive parameters of the aortic wall: a machine learning approach
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Estimation of in vivo constitutive parameters of the aortic wall: a machine learning approach
Minliang Liu, Liang Liang, Wei Sun
bioRxiv 366963; doi: https://doi.org/10.1101/366963
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Estimation of in vivo constitutive parameters of the aortic wall: a machine learning approach
Minliang Liu, Liang Liang, Wei Sun
bioRxiv 366963; doi: https://doi.org/10.1101/366963

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioengineering
Subject Areas
All Articles
  • Animal Behavior and Cognition (3606)
  • Biochemistry (7575)
  • Bioengineering (5529)
  • Bioinformatics (20806)
  • Biophysics (10333)
  • Cancer Biology (7986)
  • Cell Biology (11644)
  • Clinical Trials (138)
  • Developmental Biology (6610)
  • Ecology (10214)
  • Epidemiology (2065)
  • Evolutionary Biology (13623)
  • Genetics (9543)
  • Genomics (12851)
  • Immunology (7923)
  • Microbiology (19551)
  • Molecular Biology (7667)
  • Neuroscience (42127)
  • Paleontology (308)
  • Pathology (1258)
  • Pharmacology and Toxicology (2203)
  • Physiology (3268)
  • Plant Biology (7044)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5427)
  • Zoology (1118)