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

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A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations.

Sethuraman Sankaran¹, Alison L Marsden

¹University of California, San Diego, La Jolla, CA 92093-0411, USA.

Journal of Biomechanical Engineering

|February 10, 2011

Summary

This study introduces tools to assess how uncertainties in cardiovascular simulation inputs affect outputs like blood flow. Quantifying these uncertainties improves the reliability of hemodynamic simulations for various vascular conditions.

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Area of Science:

Computational fluid dynamics
Biomedical engineering
Medical simulations

Background:

Cardiovascular simulations predict hemodynamic parameters (velocities, wall shear stress, pressure drops).
Simulation output reliability hinges on input parameter certainty.
Uncertainties in inputs (boundary conditions, geometry, clinical data) affect simulation outcomes.

Purpose of the Study:

To develop tools for evaluating the sensitivity of hemodynamic simulation outputs to input uncertainties.
To systematically model input uncertainties and quantify confidence in simulation results.
To improve the accuracy and trustworthiness of cardiovascular flow simulations.

Main Methods:

Utilized stochastic collocation techniques to map input uncertainties into a stochastic space.
Developed an adaptive collocation algorithm using Gauss-Lobatto-Chebyshev grids to reduce computational cost.
Applied the methodology to idealized (abdominal aortic aneurysm, carotid artery bifurcation) and patient-specific (Fontan procedure) cases.

Main Results:

Quantified uncertainty in key hemodynamic features for diverse vascular models.
Demonstrated the effectiveness of the developed tools in assessing output sensitivity.
Successfully employed stochastic space representations, probability density functions (PDFs), and confidence intervals for uncertainty quantification.

Conclusions:

The developed general tools enable robust uncertainty quantification in hemodynamic simulations.
Accurate assessment of input uncertainties is crucial for reliable cardiovascular simulation data.
This approach enhances confidence in simulation-derived insights for clinical applications.