Heterogeneous mechanics of the mouse pulmonary arterial network

Lee, Pilhwa; Carlson, Brian E.; Chesler, Naomi C.; Olufsen, Mette S.; Qureshi, M. Umar; Smith, Nicolas P.; Sochi, Taha; Beard, Daniel A.

doi:10.1007/s10237-015-0757-y

Cited by 12 publications

(22 citation statements)

References 61 publications

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“…Several previous studies [3][4][5][6][7] have developed 1D fluiddynamics models predicting pulmonary blood flow and pressure. However, only a few [3,4] have aimed at devising subject-specific predictions by estimating model parameters.…”

Section: Introductionmentioning

confidence: 99%

Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

Păun

Colebank

Olufsen

et al. 2020

J. R. Soc. Interface.

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This study uses Bayesian inference to quantify the uncertainty of model parameters and haemodynamic predictions in a one-dimensional pulmonary circulation model based on an integration of mouse haemodynamic and micro-computed tomography imaging data. We emphasize an often neglected, though important source of uncertainty: in the mathematical model form due to the discrepancy between the model and the reality, and in the measurements due to the wrong noise model (jointly called ‘model mismatch’). We demonstrate that minimizing the mean squared error between the measured and the predicted data (the conventional method) in the presence of model mismatch leads to biased and overly confident parameter estimates and haemodynamic predictions. We show that our proposed method allowing for model mismatch, which we represent with Gaussian processes, corrects the bias. Additionally, we compare a linear and a nonlinear wall model, as well as models with different vessel stiffness relations. We use formal model selection analysis based on the Watanabe Akaike information criterion to select the model that best predicts the pulmonary haemodynamics. Results show that the nonlinear pressure–area relationship with stiffness dependent on the unstressed radius predicts best the data measured in a control mouse.

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Section: Introductionmentioning

confidence: 99%

Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

Păun

Colebank

Olufsen

et al. 2020

J. R. Soc. Interface.

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“…The aortic wall was simplified by using isotropic homogeneous materials. The anisotropy and heterogeneity of the aortic wall could change the level of wall contractions and stress concentrations on the aortic wall [53,54]. In addition, the effect of the aortic arch stiffness on the left ventricular (LV) function was ignored.…”

Section: Discussionmentioning

confidence: 99%

Hemodynamics and Wall Mechanics after Surgical Repair of Aortic Arch: Implication for Better Clinical Decisions

Abdullah

Lin

et al. 2019

Applied Sciences

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Graft repair of aortic coarctation is commonly used to mimic the physiological aortic arch shape and function. Various graft materials and shapes have been adopted for the surgery. The goal of this work is to quantitatively assess the impact of graft materials and shapes in the hemodynamics and wall mechanics of the restored aortic arch and its correlation with clinical outcomes. A three-dimensional aortic arch model was reconstructed from magnetic resonance images. The fluid–structure interaction (FSI) analysis was performed to characterize the hemodynamics and solid wall mechanics of the repaired aortic arch. Two graft shapes (i.e., a half-moon shape and a crescent one) were considered. Material choices of the aortic arch repair included three commonly used graft materials (i.e., polytetrafluoroethylene (PTFE) synthetic graft, CorMatrix extracellular matrix, and pulmonary homograft) as well as one native tissue serving as a control. The pathological hemodynamic parameters, in terms of the percentage area of low wall shear stress (WSS), high oscillatory shear index (OSI), and high relative residence time (RRT), were quantified to be associated with potential clinical outcomes. Results have shown that the peak von Mises stress for the aortic arch repaired by the crescent graft was 76% less than that of the half-moon graft. Flow disturbance and recirculation were also minimized with the crescent graft. Moreover, pathological hemodynamic parameters were significantly reduced with the crescent graft. The graft material mismatch with the surrounding tissue aggregated the stress concentration on the aortic wall, but had minimal impact on flow dynamics. The present work demonstrated the role and importance of the graft geometry and materials on hemodynamics and wall mechanics, which could guide optimal graft decisions towards better clinical outcomes.

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“…Most of the above studies consider application to humans. Only a few of 1D modeling studies have investigated wave propagation in systemic (Aslanidou et al, 2016) and pulmonary (Lee et al, 2016;Qureshi et al, 2017) arteries in mouse networks. To our knowledge, no studies combine disease specific imaging and hemodynamics data to predict changes in disease.…”

Section: Introductionmentioning

confidence: 99%

Hemodynamic assessment of pulmonary hypertension in mice: a model-based analysis of the disease mechanism

Qureshi

Colebank

Păun

et al. 2018

Biomech Model Mechanobiol

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This study uses a one dimensional fluid dynamics arterial network model to infer changes in hemodynamic quantities associated with pulmonary hypertension in mice. Data for this study include blood flow and pressure measurements from the main pulmonary artery for 7 control mice with normal pulmonary function and 5 hypertensive mice with hypoxia induced pulmonary hypertension. Arterial dimensions for a 21 vessel network are extracted from micro-CT images of lungs from a representative control and hypertensive mouse. Each vessel is represented by its length and radius. Fluid dynamic computations are done assuming that the flow is Newtonian, viscous, laminar, and has no swirl. The system of equations is closed by a constitutive equation relating pressure and area, using a linear model derived from stress-strain deformation in the circumferential direction assuming that the arterial walls are thin, and also an empirical nonlinear model. For each dataset, an inflow waveform is extracted from the data, and nominal parameters specifying the outflow boundary conditions are computed from mean values and characteristic time scales extracted from the data. The model is calibrated for each mouse by estimating parameters that minimize the least squares error between measured and computed waveforms. Optimized parameters are compared across the control and the hypertensive groups to characterize vascular remodeling with disease. Results show that pulmonary hypertension is associated with stiffer and less compliant proximal and distal vasculature with augmented wave reflections, and that elastic nonlinearities are insignificant in the hypertensive animal. arXiv:1712.01699v2 [physics.flu-dyn] 17 May 2018 disease progression (Castelain et al., 2001;Hunter et al., 2011). In particular, the proximal arterial stiffness is an excellent predictor of mortality in patients with pulmonary arterial hypertension (Gan et al., 2007). Quantifying relative distributions of proximal and distal arterial stiffness (or compliance) and wave reflections in elevating the mPAP and PVR is vital for understanding disease mechanisms.In this study, we setup and calibrate a mathematical model predicting wave propagation in the pulmonary vasculature in C57BL6/J male mice with normal pulmonary function (control group (CTL), n = 7) and in mice with hypoxia-induced pulmonary hypertension (hypertensive group (HPH), n = 5) (Tabima et al., 2012;Vanderpool et al., 2011). The novelty of this study is the integration of high fidelity morphometric and hemodynamic data from multiple mice with a one dimensional (1D) model of large pulmonary arteries coupled with a zero dimensional (0D) model of the vascular beds. This is achieved by incorporating available data at each stage of the modeling including network extraction, parameter estimation and model validation. The outcome is used to infer disease progression by quantifying relative changes in PVR, proximal and distal arterial stiffness, compliance, and amplitudes of wave reflections, across the two groups (CTL and HPH)...

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Heterogeneous mechanics of the mouse pulmonary arterial network

Cited by 12 publications

References 61 publications

Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation

Hemodynamics and Wall Mechanics after Surgical Repair of Aortic Arch: Implication for Better Clinical Decisions

Hemodynamic assessment of pulmonary hypertension in mice: a model-based analysis of the disease mechanism

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