Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Gao, Hao; Mangion, Kenneth; Carrick, David; Husmeier, Dirk; Luo, Xiaoyu; Berry, Colin

doi:10.1038/s41598-017-13635-2

Cited by 24 publications

(25 citation statements)

References 51 publications

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“…In addition, the lack of knowledge on patient‐specific residual strains, as well as the presence of different tissue constituents within an infarcted LV, which may possess different stress‐free configurations, present both theoretical and practical challenges for incorporating residual stresses into the constitutive model 56 and warrant further investigation. Moreover, the use of early diastole geometry as the reference geometry in the present study (assumed to be the unloaded and unstressed states) is also in accordance to the previous studies 24,26,29,56‐58 . Techniques such as the empirical formula method 59 or the backward displacement method 55 can be implemented in future studies to estimate the unloaded LV geometry.…”

Section: Discussionmentioning

confidence: 87%

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Leong

Liew

et al. 2021

Numer Methods Biomed Eng

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Infarct extension involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ, eventually leading to heart failure. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. Using human patient‐specific left‐ventricular (LV) numerical simulations established from cardiac magnetic resonance imaging (MRI) of myocardial infarction (MI) patients, the correlation between infarct extension and regional mechanics abnormality was investigated by analysing the fibre stress–strain loops (FSSLs). FSSL abnormality was characterised using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. We found that infarct extension was correlated to severely abnormal FSSL in the form of counter‐clockwise loop at the RZ close to the infarct, as indicated by negative DREW values. In regions demonstrating negative DREW values, we observed substantial fibre stretching in the isovolumic relaxation (IVR) phase accompanied by a reduced rate of systolic shortening. Such stretching in IVR phase in part of the RZ was due to its inability to withstand the high LV pressure that was still present and possibly caused by regional myocardial stiffness inhomogeneity. Further analysis revealed that the occurrence of severely abnormal FSSL due to IVR fibre stretching near the RZ‐BZ boundary was due to a large amount of surrounding infarcted tissue, or an excessively stiff IZ.

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Section: Discussionmentioning

confidence: 87%

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Leong

Liew

et al. 2021

Numer Methods Biomed Eng

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“…The biomechanical model that was initially described in Wang et al (2013) can be thought of as consisting of five parts: initial discretized LV geometry, the constitutive law (the HO law), the constitutive parameters, the finite element implementation, and corresponding initial and boundary conditions. Linking this biomechanical model to patient MRI data can allow the inference of unknown material parameters describing heart mechanics, potentially leading to improved disease diagnosis and personalized treatments (Gao, Mangion, Carrick, Husmeier, Luo and Berry, 2017).…”

Section: Left Ventricle Biomechanical Modelmentioning

confidence: 99%

Fast Parameter Inference in a Biomechanical Model of the Left Ventricle by Using Statistical Emulation

Davies

Noè

Lazarus

et al. 2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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SummaryA central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques.

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“…In general, material parameter estimation of a FEM heart model is formulated as an inverse problem [14][15][16][17][18]; for example, it requires solving a constrained optimization problem [19,20] by minimizing the mismatch between limited measured data and the FEM model predictions through finding potential material parameters. This constrained optimization problem can be solved by some gradient-based methods [21], such as the Newton method, the conjugate gradient method, and intelligent methods (also known as nature-inspired methods) [22,23], e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Many constitutive laws have been used to describe myocardial material behaviours, including isotropic models, transversely isotropic models and, more recently, orthotropic models [ 12 ]. In particular, the Holzapfel–Ogden law is a structure-based orthotropic constitutive law that not only accurately describes the mechanical behaviour of the myocardium well from various experimental data [ 13 ], but also has been successfully applied to subject-specific cardiac models purely based on in vivo routine imaging data [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have shown that myocardial property could be a potential biomarker of predicting ventricular pump function recovery post-myocardial infarction [ 14 ]. Estimation of myocardial material parameters from image-based models has attracted intensive interest by formulating a gradient-based inverse problem [ 15 ] or using machine learning (ML)-based surrogate approaches for fast parameter inferences [ 11 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium

et al. 2021

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A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure–volume and pressure–strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure–volume and pressure–strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.

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Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Cited by 24 publications

References 51 publications

The role of regional myocardial topography post‐myocardial infarction on infarct extension

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Fast Parameter Inference in a Biomechanical Model of the Left Ventricle by Using Statistical Emulation

Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium

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