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Sensitivity of Myocardial Stiffness Estimates to Inter-observer Variability in LV Geometric Modelling

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12738))

Abstract

Heart failure is known to be associated with substantial changes in the mechanical properties of the heart muscle. Biomechanical parameters, such as myocardial stiffness, have the potential to help clinicians diagnose and determine and monitor treatment options. The impact of inter-observer variability of geometric modelling on the estimation of passive myocardial stiffness has not yet been systematically investigated. We aimed to examine the sensitivity of myocardial stiffness estimates with respect to inter-observer geometric model variability. Twenty-four subjects (5 controls, 19 patients with heart failure) underwent left heart catheterisation and cardiovascular magnetic resonance (CMR) imaging. Three expert analysts independently constructed three-dimensional geometric models of the left ventricle (LV), which were used to estimate myocardial stiffness using finite element simulations that combined cine CMR data and LV pressure measurements. Bland-Altman analysis was used to assess the inter-observer effects on the reproducibility of myocardial stiffness. The inter-observer variations were ±7.69 mL/m\(^2\) and ±9.78 g/m\(^2\) for the LV end-diastolic volume and mass indices, respectively. Meanwhile, the variability ranged by up to ±0.51 kPa for inter-observer analysis on the estimated intrinsic myocardial stiffness values. Findings from this pilot study highlight the importance of the accuracy of image-based geometric modelling when estimating myocardial stiffness.

A. I. Hasaballa and T. P. Babarenda Gamage—Joint first authorship.

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Acknowledgements

We gratefully acknowledge the financial support from the Health Research Council of New Zealand (grant 17/608).

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Correspondence to Abdallah I. Hasaballa .

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Hasaballa, A.I. et al. (2021). Sensitivity of Myocardial Stiffness Estimates to Inter-observer Variability in LV Geometric Modelling. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-78710-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78709-7

  • Online ISBN: 978-3-030-78710-3

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