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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

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An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling

Sethuraman Sankaran¹, Jay D Humphrey, Alison L Marsden

¹Department of Mechanical and Aerospace Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA, United States.

Computer Methods in Applied Mechanics and Engineering

|April 30, 2013

Summary

This summary is machine-generated.

This study introduces a new computational framework for uncertainty quantification in vascular growth and remodeling (G&R) models. It efficiently identifies optimal arterial wall properties, improving disease prediction and understanding of vessel mechanics.

Keywords:

Derivative-free methods Growth and remodeling Parameter sensitivity Stochastic collocation

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Area of Science:

Computational mechanics
Biomedical engineering
Vascular biology

Background:

Computational models of vascular growth and remodeling (G&R) are crucial for predicting vessel responses to mechanical loads.
Accurate G&R models require reliable, yet often uncertain, input parameters like material properties and growth rates.
Existing methods for handling parameter uncertainty are often inefficient.

Purpose of the Study:

To develop an efficient framework for uncertainty quantification and optimal parameter selection in G&R models.
To determine optimal arterial wall material properties for maintaining homeostatic conditions under varying physiological loads.
To assess the sensitivity of G&R model outputs to parameter variability.

Main Methods:

Implemented an adaptive sparse grid stochastic collocation scheme within an established G&R solver for parameter sensitivity analysis.
Employed robust optimization by coupling the G&R simulator with sparse grid collocation and a derivative-free optimization algorithm.
Investigated the impact of uncertainty in loading conditions and material properties on arterial homeostasis.

Main Results:

Demonstrated near-linear scaling of the uncertainty quantification method with the number of parameters, outperforming Monte-Carlo methods.
Showed that arteries can achieve optimal homeostatic conditions across a range of pressure and flow alterations.
Identified prestretch of elastin and collagen as critical for maintaining homeostasis, while other properties influence response time.

Conclusions:

The developed framework provides a systematic and efficient approach for uncertainty quantification in G&R models.
Robust optimization can identify arterial wall properties that maintain homeostasis despite variability in loading conditions.
Understanding parameter sensitivities is key to improving the predictive power of vascular G&R models for disease research.