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

Rocío Rodríguez‐Cantano,Joakim Sundnes,Marie E Rognes

doi:10.1002/cnm.3178

Rocío Rodríguez‐Cantano, Joakim Sundnes + Show 1 more

Open Access

https://doi.org/10.1002/cnm.3178

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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.

Highlights

Computational modelling of the heart is a powerful technique for detailed investigations of cardiac behavior, and enables the study of mechanisms and processes that are not directly accessible by experimental methods
The local tissue structure can be determined with diffusion tensor magnetic resonance imaging (DTMRI), but this technique is still limited to ex vivo experiments
Prior to the main study focusing on model A and B as described above, we present results from the calibration of the surrogate polynomial chaos expansion (PCE) models

Summary

Introduction

Computational modelling of the heart is a powerful technique for detailed investigations of cardiac behavior, and enables the study of mechanisms and processes that are not directly accessible by experimental methods. There is currently a drive towards adapting these computational models to individual patient data, to aid in the creation of individualized diagnosis, clinical decision support, and treatment planning [1,2,3,4,5,6,7]. This model adaptation presents a number of challenges related to the lack of available data and the fact that measurable data, needed for patient-specific model input parameters, are inherently subject to measurement uncertainties or intrinsic biological variability. While this accuracy may be sufficient in the context of computational cardiac electrophysiology [26], local variations of this order have been shown to introduce sizeable variations in myofiber stresses [27]

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