Effects of dispersed fibres in myocardial mechanics, Part I: Passive response

  • Debao Guan
  • , Yuqian Mei
  • , Lijian Xu
  • , Li Cai
  • , Xiaoyu Luo
  • , Hao Gao

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

It is widely acknowledged that an imbalanced biomechanical environment can have significant effects on myocardial pathology, leading to adverse remodelling of cardiac function if it persists. Accurate stress prediction essentially depends on the strain energy function which should have competent descriptive and predictive capabilities. Previous studies have focused on myofibre dispersion, but not on fibres along other directions. In this study, we will investigate how fibre dispersion affects myocardial biomechanical behaviours by taking into account both the myofibre dispersion and the sheet fibre dispersion, with a focus on the sheet fibre dispersion. Fibre dispersion is incorporated into a widely-used myocardial strain energy function using the discrete fibre bundle approach. We first study how different dispersion affects the descriptive capability of the strain energy function when fitting to ex vivo experimental data, and then the predictive capability in a human left ventricle during diastole. Our results show that the chosen strain energy function can achieve the best goodness-of-fit to the experimental data by including both fibre dispersion. Furthermore, noticeable differences in stress can be found in the LV model. Our results may suggest that it is necessary to include both dispersion for myofibres and the sheet fibres for the improved descriptive capability to the ex vivo experimental data and potentially more accurate stress prediction in cardiac mechanics.

Original languageEnglish
Pages (from-to)3972-3993
Number of pages22
JournalMathematical Biosciences and Engineering
Volume19
Issue number4
DOIs
StatePublished - 2022

Keywords

  • Fibre dispersion
  • Left ventricle model
  • Passive myocardial mechanics
  • Strain energy function
  • Stress prediction

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