An inverse approach to determining myocardial material properties

Moulton, Michael J.; Creswell, Lawrence L.; Actis, Ricardo L.; Myers, Kent W.; Vannier, Michael W.; Szabó, Barna; Pasque, Michael K.

doi:10.1016/0021-9290(94)00144-s

Cited by 80 publications

(46 citation statements)

References 26 publications

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“…Young's modulus (Guo et al 2010) or a general linear elasticity tensor (Raghupathy and Barocas 2010), can be determined directly from the force equilibrium equation iteratively (Moulton et al 1995;Govindjee and Mihalic 1998;Kauer et al 2002;Seshaiyer and Humphrey 2003;Bosisio et al 2007;Lei and Szeri 2007;Samani and Plewes 2007;Gokhale et al 2008;Karimi et al 2008;Li et al 2009;Balocco et al 2010;Kroon 2010). …”

Section: Introductionmentioning

confidence: 99%

Anisotropic behaviour of human gallbladder walls

Hill

Ogden

et al. 2013

Journal of the Mechanical Behavior of Biomedical Materials

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Inverse estimation of biomechanical parameters of soft tissues from non-invasive measurements has clinical significance in patient-specific modelling and disease diagnosis.In this paper, we propose a fully nonlinear approach to estimate the mechanical properties of the human gallbladder wall muscles from in vivo ultrasound images. The iteration method consists of a forward approach, in which the constitutive equation is based on a modified Hozapfel-Gasser-Ogden law initially developed for arteries. Five constitutive parameters describing the two orthogonal families of fibres and the matrix material are determined by comparing the computed displacements with medical images. The optimization process is carried out using the MATLAB toolbox, a Python code, and the ABAQUS solver. The proposed method is validated with published data in artery and subsequently applied to ten human gallbladder samples. Results show that the human gallbladder wall is anisotropic during passive refilling phase, and that the peak stress is 1.6 times greater than that calculated using the linear mechanics. This discrepancy arises because the wall thickness reduces by 1.6 times during the deformation, which is not predicted by conventional linear elasticity. If the change of wall thickness is accounted for, then the linear model can used to predict the gallbladder stress and its correlation with pain. This work provides further understanding of the nonlinear characteristics of human gallbladder.

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Section: Introductionmentioning

confidence: 99%

Anisotropic behaviour of human gallbladder walls

Hill

Ogden

et al. 2013

Journal of the Mechanical Behavior of Biomedical Materials

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“…Diastolic dysfunction is caused by alterations in the active process of ventricular relaxation [4] or by alterations in the 'passive' material properties [5]. The present study extends a previously validated numerical algorithm [6], coupling nonlinear optimization, magnetic resonance imaging (MRI) radiofrequency (RF) tissue-tagging and finite element (FE) models, to the analysis of passive material properties of myocardium in the in vivo heart.…”

Section: Introductionmentioning

confidence: 92%

“…MR/ tagging allows the non-invasive measurement of deformation at many transmural points in the ventricle [25]. Our previous analysis using numerical simulations [6] indicated that the density of measurements provided by MRI tagging Figure 8. The method for determining the linear spring constant kis demonstrated.…”

Section: Measurementsmentioning

confidence: 99%

“…This problem is circumvented by calculating finite difference approximations for the elements of the Jacobian matrix: each material parameter is increased separately, a forward FE problem is solved and the difference Equations (11 ) are solved for each data point. Our previous analysis [6] demonstrated that even for nonlinear FE problems these difference approximations were sufficiently accurate to estimate a priori given parameters to within a tolerance of 0.01% in four or five iterations.…”

Section: Objective Function and Nonlinear Optimizationmentioning

confidence: 99%

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Myocardial material property determination in the in vivo heart using magnetic resonance imaging

et al. 1996

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“…Moulton et al (1995) used a combination of finite element model, nonlinear optimization algorithm, and a set of strains obtained by magnetic resonance imaging (MRI) to determine passive myocardial material properties. Miller and Chinzei (1997) conducted pressure experiments on brain tissue to model the destructions that may happen during brain surgery.…”

Section: Introductionmentioning

confidence: 99%

Determination of material parameters of isotropic and anisotropic hyper-elastic materials using boundary measured data

Hajhashemkhani

Hematiyan

2015

jtam

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Identification of mechanical properties of isotropic and anisotropic materials that demonstrate non-linear elastic behavior, such as rubbers and soft tissues of human body, is critical for many industrial and medical purposes. In this paper, a method is presented to obtain the mechanical constants of Mooney-Rivlin and Holzapfel hyper-elastic material models which are employed to describe the behavior of isotropic and anisotropic hyper-elastic materials, respectively. By using boundary measured data from a sample with non-standard geometry, and by using an iterative inverse analysis technique, the material constants are obtained. The method uses the results of different experiments simultaneously to obtain the material parameters more accurately. The effectiveness of the proposed method is demonstrated through three examples. In the two first examples, the simulated measured data are used, while in the third example, the experimental data obtained from a polyvinyl alcohol sample are used.

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An inverse approach to determining myocardial material properties

Cited by 80 publications

References 26 publications

Anisotropic behaviour of human gallbladder walls

Anisotropic behaviour of human gallbladder walls

Myocardial material property determination in the in vivo heart using magnetic resonance imaging

Determination of material parameters of isotropic and anisotropic hyper-elastic materials using boundary measured data

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