Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

Molléro, Roch; Neumann, Dominik; Rohé, Marc Michel; Datar, Manasi; Lombaert, Hervé; Ayache, Nicholas; Comanicìu, Dorin; Ecabert, Olivier; Chinali, Marcello; Rinelli, Gabriele; Pennec, Xavier; Sermesant, Maxime; Mansi, Tommaso

doi:10.1007/978-3-319-20309-6_51

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…PCA may offer a more realistic quantification of the variability of the fiber perturbation field by its mean and covariance matrix sampled from a cardiac diffusion tensor imaging (DTI) population distribution. 28 Third, other extensions of this study should include not just the filling phase of the heart but also the active contraction of the muscle in the cardiac cycle, as well as taking into account in the same model the uncertainty emerging from both input material parameters and the fiber architecture. On the methods side, the use of a fixed PCE basis may lead to unaffordable computational loads as the number of uncertain parameters grows.…”

Section: Discussionmentioning

confidence: 99%

“…Second, in order to quantify the variability of the fiber architecture, while we here considered a truncated Karhunen-Loéve expansion, an alternative would be a Principal Component Analysis (PCA). PCA may offer a more realistic quantification of the variability of the fiber perturbation field by its mean and covariance matrix sampled from a cardiac diffusion tensor imaging (DTI) population distribution [28]. Third, other extensions of this study should include not just the filling phase of the heart but also the active contraction of the muscle in the cardiac cycle, as well as taking into account in the same model the uncertainty emerging from both input material parameters and the fiber architecture.…”

Section: Discussionmentioning

confidence: 99%

“…The reported variability includes both true variability of the fiber structure and errors due to acquisition and image registration. Similarly, Molléro et al [28] estimated and represented the uncertainty of cardiac fiber architecture originating from the lack of data for a given patient using the mean and principal modes of variations among a given population of healthy hearts.…”

Section: Introductionmentioning

confidence: 99%

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

Rodríguez‐Cantano

Sundnes

Rognes

2019

Numer Methods Biomed Eng

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rodríguez‐Cantano

Sundnes

Rognes

2019

Numer Methods Biomed Eng

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“…We performed 3D cardiac modelling for 21 of these patients. A high-resolution biventricular tetrahedral mesh of the patient's heart morphology was extracted as in [6] from the pre-ingestion MRI at T 1 , made of around 15 000 nodes. On this mesh, a myocardial fibre direction can be defined at each node of the mesh (see Fig 1a), by varying the elevation angles of the fibre across the myocardial wall from α 1 on the epicardium to α 2 degrees on the endocardium.…”

Section: D Electromechanical Cardiac Modelmentioning

confidence: 99%

Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion

Molléro

Hauser

Pennec

et al. 2017

Functional Imaging and Modelling of the Heart

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Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However the number of simulation parameters in these models can be high and expert knowledge is required to properly design studies involving these models, and analyse the results. In particular it is important to know how the parameters vary in various clinical or physiological settings. In this paper we build a data-driven model of cardiovascular parameter evolution during digestion, from a clinical study involving more than 80 patients. We first present a method for longitudinal parameter estimation in 3D cardiac models, which we apply to 21 patient-specific hearts geometries at two instants of the study, for 6 parameters (two fixed and four time-varying parameters). From these personalised hearts, we then extract and validate a law which links the changes of cardiac output and heart rate under constant arterial pressure to the evolution of these parameters, thus enabling the fast simulation of hearts during digestion for future patients.

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“…Deterministic methods achieve similar objectives through the perturbation of variables used in the analysis. The influence of input parameters was reported when propagating the variations of the regularization weight of a regression [18], or the fibers orientation in a computational model [25].…”

Section: B Uncertainty In the Predictionmentioning

confidence: 99%

Infarct Localization From Myocardial Deformation: Prediction and Uncertainty Quantification by Regression From a Low-Dimensional Space

Duchateau

Craene

Allain

et al. 2016

IEEE Trans. Med. Imaging

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Diagnosing and localizing myocardial infarct is crucial for early patient management and therapy planning. We propose a new method for predicting the location of myocardial infarct from local wall deformation, which has value for risk stratification from routine examinations such as (3D) echocardiography. The pipeline combines non-linear dimensionality reduction of deformation patterns and two multi-scale kernel regressions. Confidence in the diagnosis is assessed by a map of local uncertainties, which integrates plausible infarct locations generated from the space of reduced dimensionality. These concepts were tested on 500 synthetic cases generated from a realistic cardiac electromechanical model, and 108 pairs of 3D echocardiographic sequences and delayed-enhancement magnetic resonance images from real cases. Infarct prediction is made at a spatial resolution around 4 mm, more than 10 times smaller than the current diagnosis, made regionally. Our method is accurate, and significantly outperforms the clinically-used thresholding of the deformation patterns (on real data: sensitivity/specificity of 0.828/0.804, area under the curve: 0.909 versus 0.742 for the most predictive strain component). Uncertainty adds value to refine the diagnosis and eventually re-examine suspicious cases.

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Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

Cited by 5 publications

References 16 publications

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

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

Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion

Infarct Localization From Myocardial Deformation: Prediction and Uncertainty Quantification by Regression From a Low-Dimensional Space

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