Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function

Wang, Vicky Y.; Lam, H.; Ennis, Daniel B.; Cowan, Brett R.; Young, Alistair A.; Nash, Martyn P.

doi:10.1016/j.media.2009.07.006

Cited by 160 publications

(139 citation statements)

References 42 publications

(73 reference statements)

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“…A more flexible way is to embed the pressure data into the objective function, which will be investigated in the future. This framework is similar to other parameter estimation frameworks (Chabiniok et al, 2011;Delingette et al, 2012;Liu and Shi, 2009;Wang et al, 2009;Xi et al, 2011), and therefore suffers from the curse of dimensionality. Although the subplex method has already helped to alleviate the problem by decomposing the high-dimensional space into subspaces, the numbers of iterations required are still large, and it will be computationally very challenging with more number of zones.…”

Section: Discussionmentioning

confidence: 92%

“…In Chabiniok et al (2011), the regional maximum contraction stresses were estimated using reduced-order unscented Kalman filtering (rUKF) from porcine cine MRI. In Wang et al (2009), homogeneous passive material stiffness of a nonlinear transversely isotropic model was estimated with a sequential quadratic programming (SQP) method from tagged MRI and ex vivo DT-MRI of canine hearts. In Xi et al (2011), the performances of rUKF and SQP for the estimation of stiffness parameters were compared on synthetic data.…”

Section: Introductionmentioning

confidence: 99%

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Velocity-based cardiac contractility personalization from images using derivative-free optimization

Wong

Sermesant

Rhode

et al. 2015

Journal of the Mechanical Behavior of Biomedical Materials

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Model personalization is a key aspect for biophysical models to impact clinical practice, and cardiac contractility personalization from medical images is a major step in this direction. Existing gradient-based optimization approaches show promising results of identifying the maximum contractility from images, but the contraction and relaxation rates are not accounted for. A main reason is the limited choices of objective functions when their gradients are required. For complicated cardiac models, analytical evaluations of gradients are very difficult if not impossible, and finite difference approximations are computationally expensive and may introduce numerical difficulties. By removing such limitations with derivative-free optimization, we found that a velocity-based objective function can properly identify regional maximum contraction stresses, contraction rates, and relaxation rates simultaneously with intact model complexity. Experiments on synthetic data show that the parameters are better identified using the velocity-based objective function than its position-based counterpart, and the proposed framework is insensitive to initial parameters with the adopted derivative-free optimization algorithm. Experiments on clinical data show that the framework can provide personalized contractility parameters which are consistent with the underlying physiologies of the patients and healthy volunteers.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Velocity-based cardiac contractility personalization from images using derivative-free optimization

Wong

Sermesant

Rhode

et al. 2015

Journal of the Mechanical Behavior of Biomedical Materials

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“…The experimental data, which is fully described described in [7], was kindly provided by the University of Auckland as part of the mechanical challenge at the STACOM'14 workshop.…”

Section: Methodsmentioning

confidence: 99%

Fully-Coupled Electromechanical Simulations of the LV Dog Anatomy Using HPC: Model Testing and Verification

Aguado-Sierra

Santiago

Rivero

et al. 2015

Lecture Notes in Computer Science

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Abstract. Verification of electro-mechanic models of the heart require a good amount of reliable, high resolution, thorough in-vivo measurements. The detail of the mathematical models used to create simulations of the heart beat vary greatly. Generally, the objective of the simulation determines the modeling approach. However, it is important to exactly quantify the amount of error between the various approaches that can be used to simulate a heart beat by comparing them to ground truth data. The more detailed the model is, the more computing power it requires, we therefore employ a high-performance computing solver throughout this study. We aim to compare models to data measured experimentally to identify the effect of using a mathematical model of fibre orientation versus the measured fibre orientations using DT-MRI. We also use simultaneous endocardial stimuli vs an instantaneous myocardial stimulation to trigger the mechanic contraction. Our results show that synchronisation of the electrical and mechanical events in the heart beat are necessary to create a physiological timing of hemodynamic events. Synchronous activation of all of the myocardium provides an unrealistic timing of hemodynamic events in the cardiac cycle. Results also show the need of establishing a protocol to quantify the zero-pressure configuration of the left ventricular geometry to initiate the simulation protocol; however, the predicted zero-pressure configuration of the same geometry was different, depending on the origin of the fibre field employed.

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“…The estimation of hyperelastic material constants from images was unavailable until recently. In [7], [8], homogeneous, hyperelastic, and transversely isotropic material properties were estimated. A finite deformation elasticity problem was solved for early diastolic filling to simulate the passive mechanics of the left ventricle, and the estimation was performed by matching the predicted motion of material points derived from tagged MRI.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, homogeneous properties are insufficient for model personalization. Furthermore, the interaction with active contraction was not accounted for in [5], [7], [8], and all frameworks used displacements as measurements.…”

Section: Introductionmentioning

confidence: 99%

Strain-Based Regional Nonlinear Cardiac Material Properties Estimation from Medical Images

Wong

Relan

Wang

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Model personalization is essential for model-based surgical planning and treatment assessment. As alteration in material elasticity is a fundamental cause to various cardiac pathologies, estimation of material properties is important to model personalization. Although the myocardium is heterogeneous, hyperelastic, and orthotropic, existing image-based estimation frameworks treat the tissue as either heterogeneous but linear, or hyperelastic but homogeneous. In view of these, we present a physiology-based framework for estimating regional, hyperelastic, and orthotropic material properties. A cardiac physiological model is adopted to describe the macroscopic cardiac physiology. By using a strainbased objective function which properly reflects the change of material constants, the regional material properties of a hyperelastic and orthotropic constitutive law are estimated using derivative-free optimization. Experiments were performed on synthetic and real data to show the characteristics of the framework.

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Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function

Cited by 160 publications

References 42 publications

Velocity-based cardiac contractility personalization from images using derivative-free optimization

Velocity-based cardiac contractility personalization from images using derivative-free optimization

Fully-Coupled Electromechanical Simulations of the LV Dog Anatomy Using HPC: Model Testing and Verification

Strain-Based Regional Nonlinear Cardiac Material Properties Estimation from Medical Images

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