Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response

Abstract

Computational cardiac modelling is a mature area of biomedical computing and is currently evolving from a pure research tool to aiding in clinical decision making. Assessing the reliability of computational model predictions is a key factor for clinical use, and uncertainty quantification (UQ) and sensitivity analysis are important parts of such an assessment. In this study, we apply UQ in computational heart mechanics to study uncertainty both in material parameters characterizing global myocardial stiffness and in the local muscle fiber orientation that governs tissue anisotropy. The uncertainty analysis is performed using the polynomial chaos expansion (PCE) method, which is a nonintrusive meta‐modeling technique that surrogates the original computational model with a series of orthonormal polynomials over the random input parameter space. In addition, in order to study variability in the muscle fiber architecture, we model the uncertainty in orientation of the fiber field as an approximated random field using a truncated Karhunen‐Loéve expansion. The results from the UQ and sensitivity analysis identify clear differences in the impact of various material parameters on global output quantities. Furthermore, our analysis of random field variations in the fiber architecture demonstrate a substantial impact of fiber angle variations on the selected outputs, highlighting the need for accurate assignment of fiber orientation in computational heart mechanics models.

Effect of myofibre architecture on ventricular pump function by using a neonatal porcine heart model: from DT-MRI to rule-based methods

“…Large differences can be expected when using literature-based fibre structures and dispersion parameters compared with case LDDMM. Those different results highlight the necessity of use realistic myofibre structure for personalized cardiac modelling as demonstrated in other studies [15,20,21,24]. In case LDDMM, the high active fibre stresses at both epicardial and endocardial surfaces (figure 7a) can potentially enhance the long-axis shortening and also apical twist (figure 6d).…”

“…Rodríguez-Cantano et al [24] argued that RBM tends to exaggerate myofibre-layered architecture and the passive stiffness of the ventricle, while DT-MRI-measured fibres may underestimate ventricular stiffness due to measurement noise and uncertainties. We find that when taking into account the cross-fibre contraction in the case RBM uni , we can achieve similar systolic contraction as case LDDMM (figure 9) with less heterogeneous stress patterns.…”

Uncertainty in model‐based treatment decision support: Applied to aortic valve stenosis

“…50 Uncertainty quantification of model output is already becoming more prolific in cardiovascular research. [51][52][53] However, uncertainty is often propagated from assumed input distributions, rather than based on optimisation uncertainty, which is in turn dependent on the quality (noise, temporal resolution, availability) to which the model is optimized. In this work, we have shown that the results of Kalman filter-based optimisation can be translated into probability density estimates.…”

Impact of uncertainties in cardiac mechanics simulations

“…Trabalhos anteriores tem estudado o impacto de incertezas em simulações da mecânica cardíaca. O estudo de [Rodríguez-Cantano et al 2019], por exemplo, analisou o impacto de incertezas da orientação de fibras e propriedades do tecido cardíaco em simulações da mecânica cardíaca durante a fase de preenchimento do ventrículo esquerdo.…”