Estimation of passive and active properties in the human heart using 3D tagged MRI

Liya Asner, Myrianthi Hadjicharalambous, Radomir Chabiniok, Devis Peresutti, Eva Sammut, James Wong, Gerald Carr-White, Philip Chowienczyk, Jack Lee, Andrew King, Nicolas Smith, Reza Razavi, David Nordsletten

Research output: Contribution to journalArticlepeer-review

Abstract

Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and—through patient-specific modelling—derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.

Original languageEnglish (US)
Pages (from-to)1121-1139
Number of pages19
JournalBiomechanics and Modeling in Mechanobiology
Volume15
Issue number5
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Keywords

  • 3D tagged MRI
  • Cardiac mechanics
  • Parameter estimation
  • Patient-specific modelling

ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Mechanical Engineering

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