Improved identifiability of myocardial material parameters by an energy-based cost function

Anastasia Nasopoulou; Anoop Shetty; Jack Lee; David Nordsletten; Christopher Aldo Rinaldi; Pablo Lamata de la Orden; Steven Niederer

doi:10.1007/s10237-016-0865-3

Improved identifiability of myocardial material parameters by an energy-based cost function

Anastasia Nasopoulou, Anoop Shetty, Jack Lee, David Nordsletten, Christopher Aldo Rinaldi, Pablo Lamata de la Orden^*, Steven Niederer

^*Corresponding author for this work

GSTT Guy's and St Thomas' NHS Foundation Trust

Research output: Contribution to journal › Article › peer-review

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Abstract

Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the HF cases and (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the healthy case.

Original language	English
Pages (from-to)	1-18
Number of pages	18
Journal	Biomechanics and Modeling in Mechanobiology
DOIs	https://doi.org/10.1007/s10237-016-0865-3
Publication status	Accepted/In press - 10 Feb 2017

Keywords

Myocardium
Parameter estimation
Passive constitutive equations
Patient-specific modelling

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10.1007/s10237-016-0865-3Licence: CC BY

Improved identifiability of myocardial_NASOPOULOU_Firstonline10February2017_GOLD VoRFinal published version, 3.36 MBLicence: CC BY

Cite this

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abstract = "Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the HF cases and (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the healthy case.",

keywords = "Myocardium, Parameter estimation, Passive constitutive equations, Patient-specific modelling",

author = "Anastasia Nasopoulou and Anoop Shetty and Jack Lee and David Nordsletten and Rinaldi, {Christopher Aldo} and {Lamata de la Orden}, Pablo and Steven Niederer",

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AU - Nasopoulou, Anastasia

AU - Shetty, Anoop

AU - Lee, Jack

AU - Nordsletten, David

AU - Rinaldi, Christopher Aldo

AU - Lamata de la Orden, Pablo

AU - Niederer, Steven

PY - 2017/2/10

Y1 - 2017/2/10

N2 - Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the HF cases and (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the healthy case.

AB - Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the HF cases and (Formula presented.) and (Formula presented.) ((Formula presented.), (Formula presented.), (Formula presented.)) for the healthy case.

KW - Myocardium

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KW - Passive constitutive equations

KW - Patient-specific modelling

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ER -

Improved identifiability of myocardial material parameters by an energy-based cost function

Abstract

Keywords

UN SDGs

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