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Optimization Framework for Patient-Specific Cardiac Modeling

  • Joshua Mineroff
  • Andrew D. McCulloch
  • David Krummen
  • Baskar Ganapathysubramanian
  • Adarsh KrishnamurthyEmail author
Article
  • 99 Downloads

Abstract

Purpose

Patient-specific models of the heart can be used to improve the diagnosis of cardiac diseases, but practical application of these models can be impeded by the computational costs and numerical uncertainties of fitting mechanistic models to clinical measurements from individual patients. Reliable and efficient tuning of these models within clinically appropriate error bounds is a requirement for practical deployment in the time-constrained environment of the clinic.

Methods

We developed an optimization framework to tune parameters of patient-specific mechanistic models using routinely-acquired non-invasive patient data more efficiently than manual methods. We employ a hybrid particle swarm and pattern search optimization algorithm, but the framework can be readily adapted to use other optimization algorithms.

Results

We apply the proposed framework to tune full-cycle lumped parameter circulatory models using clinical data. We show that our framework can be easily adapted to optimize cross-species models by tuning the parameters of the same circulation model to four canine subjects.

Conclusions

This work will facilitate the use of biomechanics and circulatory cardiac models in both clinical and research environments by ameliorating the tedious process of manually fitting the parameters.

Keywords

Optimization Cardiac biomechanics Lumped-parameter circulation model Patient-specific modeling 

Notes

Funding

This research was supported in part by the National Biomedical Computation Resource, NIH Grant 8 P41 GM103426-21 (Amaro and McCulloch), by the UC Center for Accelerated Innovation under NIH Grant 4 U54 HL11-9893, NIH Grant 1 R01 HL131753 (Segars, Krishnamurthy, McCulloch), by NSF Grant 1750865 (Krishnamurthy), and by the Joseph C. and Elizabeth A. Anderlik Professorship at Iowa State University (Mineroff). We would also like to show our gratitude to Dr. Judy Vance at Iowa State University for her extraordinary support.

Conflict of interest

Andrew D. McCulloch is a co-founder, equity holders and scientific advisory board member of Insilicomed Inc. and Vektor Medical, licensees of UCSD software used in this research. This relationship has been disclosed to the University of California San Diego and is overseen by an independent conflict of interest management subcommittee appointed by the university. Joshua Mineroff, David Krummen, Baskar Ganapathysubramanian, and Adarsh Krishnamurthy have declared that no competing interests exist.

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

13239_2019_428_MOESM1_ESM.pdf (1.8 mb)
Electronic supplementary material 1 (PDF 1830 kb)

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Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIowa State UniversityAmesUSA
  2. 2.Bioengineering and MedicineUniversity of California, San DiegoLa JollaUSA
  3. 3.Department of Medicine (Cardiology)University of California, San DiegoLa JollaUSA

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