Investigating the reference domain influence in personalised models of cardiac mechanics

Hadjicharalambous, Myrianthi; Stoeck, Christian T.; Weisskopf, Miriam; Cesarovic, Nikola; Ioannou, Eleftherios; Vavourakis, Vasileios; Nordsletten, David

doi:10.1007/s10237-021-01464-2

Cited by 10 publications

(9 citation statements)

References 43 publications

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“…In early diastole, residual active stress may be present and in all phases of diastolic filling, the cavity pressure is never zero. Although passive parameter estimates have been shown to be minimally affected by changing the reference state from end-systolic to early-diastolic geometries (Hadjicharalambous et al, 2014b ), the impact of the choice of reference state is examined further in Hadjicharalambous et al ( 2021 ) and should be assessed in biventricular patient-specific modelling.…”

Section: Discussionmentioning

confidence: 99%

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An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

et al. 2021

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Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

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Section: Discussionmentioning

confidence: 99%

“…These methods are dependent on the choice of material law, constitutive parameters and boundary conditions. An analysis illustrating the impact of these choices and ramifications of boundary conditions is presented in Hadjicharalambous et al ( 2021 ).…”

Section: Methodsmentioning

confidence: 99%

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

et al. 2021

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“… 2016 ; Hadjicharalambous et al. 2017 , 2021 ). None of the above reparameterizations are based on a comprehensive sensitivity study, but from empirical observations and intuitive insights of myocardial behaviours.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics

Lazarus

Dalton

Husmeier

et al. 2022

Biomech Model Mechanobiol

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Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model.

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“…STL models of the truncated ventricles ( Section 2.3.1 ) generated at the end-systolic phase define the unloaded configuration ( Hadjicharalambous et al, 2021 ). This truncation was performed to avoid modeling the basal regions of the heart, including the valves and atria, which are often poorly resolved in the short-axis MRI sequence and are represented with lumped-parameter circuits.…”

Section: Methodsmentioning

confidence: 99%

Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension

Tossas-Betancourt

Shavik

et al. 2022

Front. Physiol.

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Pulmonary arterial hypertension (PAH) is a complex disease involving increased resistance in the pulmonary arteries and subsequent right ventricular (RV) remodeling. Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models. It is important to identify metrics that capture and quantify these interactions to inform our understanding of this disease as well as potentially facilitate patient stratification. Towards this end, we developed and calibrated two multi-scale high-resolution closed-loop computational models using open-source software: a high-resolution arterial model implemented using CRIMSON, and a high-resolution ventricular model implemented using FEniCS. Models were constructed with clinical data including non-invasive imaging and invasive hemodynamic measurements from a cohort of pediatric PAH patients. A contribution of this work is the discussion of inconsistencies in anatomical and hemodynamic data routinely acquired in PAH patients. We proposed and implemented strategies to mitigate these inconsistencies, and subsequently use this data to inform and calibrate computational models of the ventricles and large arteries. Computational models based on adjusted clinical data were calibrated until the simulated results for the high-resolution arterial models matched within 10% of adjusted data consisting of pressure and flow, whereas the high-resolution ventricular models were calibrated until simulation results matched adjusted data of volume and pressure waveforms within 10%. A statistical analysis was performed to correlate numerous data-derived and model-derived metrics with clinically assessed disease severity. Several model-derived metrics were strongly correlated with clinically assessed disease severity, suggesting that computational models may aid in assessing PAH severity.

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Investigating the reference domain influence in personalised models of cardiac mechanics

Cited by 10 publications

References 43 publications

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics

Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension

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