arXiv subjects

Amirreza Kachabi

Publications and source records attributed to Amirreza Kachabi.

4 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↗

arXiv2024

New approach method for solving nonlinear differential equations of blood flow with nanoparticle in presence of magnetic field

In this paper, effect of physical parameters in presence of magnetic field on heat transfer and flow of third grade non-Newtonian Nanofluid in a porous medium with annular cross sectional analytically has been investigated. The viscosity of Nanofluid categorized in 3 model include constant model and variable models with temperature that in variable category Reynolds Model and Vogel's Model has been used to determine the effect of viscosity in flow filed. analytically solution for velocity, temperature, and nanoparticle concentration are developed by Akbari-Ganji's Method (AGM) that has high proximity with numerical solution (Runge-Kutta 4th-order). Physical parameters that used for extract result for non dimensional variables of nonlinear equations are pressure gradient, Brownian motion parameter, thermophoresis parameter, magnetic field intensity and Grashof number. The results show that the increase in the pressure gradient and Thermophoresis parameter and decrease in the Brownian motion parameter cause the rise in the velocity profile. Also the increase in the Grashof number and decrease in MHD parameter cause the rise in the velocity profile. Furthermore, either increase in Thermophoresis or decrease in Brownian motion parameters results in enhancement in nanoparticle concentration. The highest value of velocity is observed when the Vogel's Model is used for viscosity.

physics.flu-dyn↗