arXiv subjects

Naomi C. Chesler

Publications and source records attributed to Naomi C. Chesler.

6 recordsLinked to original sources

arXiv2026

A Bayesian Framework for Uncertainty-Aware Estimation of Main Pulmonary Artery Velocity Profiles from Phase-Contrast MRI

Computational cardiovascular flow models are highly sensitive to prescribed inlet velocity profiles. While imaging-derived velocity fields provide physiologically realistic information, they can introduce increased preprocessing complexity, imaging noise, and computational burden. Simplified analytical formulations are computationally efficient but may not fully capture subject-specific flow characteristics. In this study, we present an uncertainty-aware framework that combines two-dimensional phase-contrast magnetic resonance imaging (2D PC-MRI) with mechanistic velocity-profile formulations to generate subject-specific pulmonary artery velocity representations. Imaging-derived radial velocity distributions were constructed from main pulmonary artery (MPA) PC-MRI data in canine and swine subjects using elliptical radial binning and normalization. Power-law and Womersley velocity-profile formulations were fitted within a Bayesian inference framework while accounting for uncertainty associated with imaging measurements and model representation. The two formulations were compared using regional and global weighted root mean square error (wRMSE) metrics. Both models demonstrated close agreement with the imaging-derived velocity profiles across subjects. Although the Womersley formulation provided greater flexibility near the vessel wall, it did not result in statistically significant improvements in fitting performance compared with the simpler power-law model. The proposed framework provides low-dimensional, physiologically interpretable, and uncertainty-aware velocity-profile representations that may serve as computationally efficient alternatives for subject-specific cardiovascular flow modeling.

stat.AP↗

arXiv2025

Bayesian Parameter Inference and Uncertainty Quantification for a Computational Pulmonary Hemodynamics Model Using Gaussian Processes

Subject-specific modeling is a powerful tool in cardiovascular research, providing insights beyond the reach of current clinical diagnostics. Limitations in available clinical data require the incorporation of uncertainty into models to improve guidance for personalized treatments. However, for clinical relevance, such modeling must be computationally efficient. In this study, we used a one-dimensional (1D) fluid dynamics model informed by experimental data from a dog model of chronic thromboembolic pulmonary hypertension (CTEPH), incorporating measurements from multiple subjects under both baseline and CTEPH conditions. Surgical intervention can alleviate CTEPH, yet patients with microvascular disease (e.g., remodeling and narrowing of small vessels) often exhibit persistent pulmonary hypertension, highlighting the importance of assessing microvascular disease severity. Thus, each lung was modeled separately to account for the heterogeneous nature of CTEPH, allowing us to explore lung-specific microvascular narrowing and resistance. We compared inferred parameters between baseline and CTEPH and examined their correlation with clinical markers of disease severity. To accelerate model calibration, we employed Gaussian process (GP) emulators, enabling the estimation of microvascular parameters and their uncertainties within a clinically feasible timeframe. Our results demonstrated that CTEPH leads to heterogeneous microvascular adaptation, reflected in distinct parameter shifts. Notably, the changes in model parameters strongly correlated with disease severity, especially in the lung previously reported to have more advanced disease. This framework provides a rapid, uncertainty-aware method for evaluating microvascular dysfunction in CTEPH and may support more targeted treatment strategies within a timeframe suitable for clinical application.

stat.AP↗

arXiv2024

Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH

Microvascular disease is a contributor to persistent pulmonary hypertension in those with chronic thromboembolic pulmonary hypertension (CTEPH). The heterogenous nature of the micro and macrovascular defects motivates the use of personalized computational models, which can predict flow dynamics within multiple generations of the arterial tree and into the microvasculature. Our study uses computational hemodynamics models and Gaussian processes for rapid, subject-specific calibration using retrospective data from a large animal model of CTEPH. Our subject-specific predictions shed light on microvascular dysfunction and arterial wall shear stress changes in CTEPH.

q-bio.QM↗

arXiv2023

Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics

Computational hemodynamics models are becoming increasingly useful in the management and prognosis of complex, multiscale pathologies, including those attributed to the development of pulmonary vascular disease. However, diseases like pulmonary hypertension are heterogeneous, and affect both the proximal arteries and veins as well as the microcirculation. Simulation tools and the data used for model calibration are also inherently uncertain, requiring a full analysis of the sensitivity and uncertainty attributed to model inputs and outputs. Thus, this study quantifies model sensitivity and output uncertainty in a multiscale, pulse-wave propagation model of pulmonary hemodynamics. Our pulmonary circuit model consists of fifteen proximal arteries and twelve proximal veins, connected by a two-sided, structured tree model of the distal vasculature. We use polynomial chaos expansions to expedite the sensitivity and uncertainty quantification analyses and provide results for both the proximal and distal vasculature. Our analyses provide uncertainty in blood pressure, flow, and wave propagation phenomenon, as well as wall shear stress and cyclic stretch, both of which are important stimuli for endothelial cell mechanotransduction. We conclude that, while nearly all the parameters in our system have some influence on model predictions, the parameters describing the density of the microvascular beds have the largest effects on all simulated quantities in both the proximal and distal circulation.

q-bio.TO↗

arXiv2022

Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area and wall thickness in normal and hypertensive murine left pulmonary arteries

Pulmonary hypertension is a cardiovascular disorder manifested by elevated arterial blood pressure together with vessel wall stiffening and thickening due to alterations in collagen, elastin and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study formulates and systematically reduces a nonlinear elastic structural wall model for a large pulmonary artery, generating a novel pressure-area relation capturing remodeling in type 3 pulmonary hypertension. The model is calibrated using {\em ex vivo} measurements of vessel diameter and wall thickness changes, under controlled flow conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation and subset selection are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive group. The novel pressure-area relation developed in this study has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

q-bio.TO↗

arXiv2021

Numerical predictions of shear stress and cyclic stretch in the healthy pulmonary vasculature

Isolated post-capillary pulmonary hypertension (Ipc-PH) occurs due to left heart failure, which contributes to 1 out of every 9 deaths in the United States. In some patients, through unknown mechanisms, Ipc-PH transitions to combined pre-/post-capillary PH (Cpc-PH), diagnosed by an increase in pulmonary vascular resistance and associated with a dramatic increase in mortality. We hypothesize that altered mechanical forces and subsequent vasoactive signaling in the pulmonary capillary bed drive the transition from Ipc-PH to Cpc-PH. However, even in a healthy pulmonary circulation, the mechanical forces in the smallest vessels (the arterioles, venules, and capillary bed) have not been quantitatively defined. This study is the first to examine this question via a computational fluid dynamics model of the human pulmonary arteries, veins, arterioles, and venules. Using this model we predict temporal and spatial dynamics of cyclic stretch and wall shear stress. In the large vessels, numerical simulations show that shear stress increases coincides with larger flow and pressure. In the microvasculature, we found that as vessel radius decreases, shear stress increases and flow decreases. In arterioles, this corresponds with lower pressures; however, the venules and smaller veins have higher pressure than larger veins. Our model provides predictions for pressure, flow, shear stress, and cyclic stretch that provides a way to analyze and investigate hypotheses related to disease progression in the pulmonary circulation.

q-bio.TO↗