Distribution of normal human left ventricular myofiber stress at end diastole and end systole: A target for in silico design of heart failure treatments

  • Martin Genet
  • , Lik Chuan Lee
  • , Rebecca Nguyen
  • , Henrik Haraldsson
  • , Gabriel Acevedo-Bolton
  • , Zhihong Zhang
  • , Liang Ge
  • , Karen Ordovas
  • , Sebastian Kozerke
  • , Julius M. Guccione

Research output: Contribution to journalArticlepeer-review

Abstract

Ventricular wall stress is believed to be responsible for many physical mechanisms taking place in the human heart, including ventricular remodeling, which is frequently associated with heart failure. Therefore, normalization of ventricular wall stress is the cornerstone of many existing and new treatments for heart failure. In this paper, we sought to construct reference maps of normal ventricular wall stress in humans that could be used as a target for in silico optimization studies of existing and potential new treatments for heart failure. To do so, we constructed personalized computational models of the left ventricles of five normal human subjects using magnetic resonance images and the finite-element method. These models were calibrated using left ventricular volume data extracted from magnetic resonance imaging (MRI) and validated through comparison with strain measurements from tagged MRI (950 ± 170 strain comparisons/subject). The calibrated passive material parameter values were C0 = 0.115 ± 0.008 kPa and B0 = 14.4 ± 3.18; the active material parameter value was Tmax = 143 ± 11.1 kPa. These values could serve as a reference for future construction of normal human left ventricular computational models. The differences between the predicted and the measured circumferential and longitudinal strains in each subject were 3.4 ± 6.3 and 0.5 ± 5.9%, respectively. The predicted end-diastolic and endsystolic myofiber stress fields for the five subjects were 2.21 ± 0.58 and 16.54 ± 4.73 kPa, respectively. Thus these stresses could serve as targets for in silico design of heart failure treatments.

Original languageEnglish
Pages (from-to)142-152
Number of pages11
JournalJournal of Applied Physiology
Volume117
Issue number2
DOIs
Publication statusPublished - 15 Jul 2014
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Computational modeling
  • Normal human subjects
  • Patient-specific modeling
  • Tagged MRI

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