Cardiac biomechanics modelling

Cardiac biomechanics have proven particularly useful for improving our understanding of heart function in health and disease, providing a quantitative framework for studying cardiac pathophysiology. Personalised cardiac mechanics models, combining mathematical modelling with clinical data, are becoming increasingly popular as a tool for noninvasively assessing biomechanical quantities. Despite substantial progress in the field, important model uncertainties remain, including the unknown zero-pressure domain. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse FEA, which entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data.

In our recent research paper, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains, and stresses, while they highlight the importance of boundary conditions accounting for the constraining influence of the ribs.

Relevant works:

Hadjicharalambous et al. 2021. Biomech Model Mechanobiol, doi: 10.1007/s10237-021-01464-2

Hadjicharalambous et al. 2017. Ann Biomed Eng, doi: 10.1007/s10439-016-1721-4

Vavourakis et al. 2016. Ann Biomed Eng, doi: 10.1007/s10439-015-1405-5

CINE images of the end-diastolic (left), end-systolic (middle) and ground-truth reference (right) geometries, with the boundaries of the personalised FE models overlaid, in short-axis (top) and 4-chamber (bottom) views.
Views of FE simulated inverse geometries overlaid onto the in-situ images, with simulations neglecting (top) or accounting for the rib-cage influence (bottom).

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