Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator.
Andrea Arnold1, Christina Battista2, Daniel Bia3
1Department of Mathematics, North Carolina State University, 2108 SAS Hall, 2311 Stinson Drive, Box 8205, Raleigh, NC 27695-8205
Journal of Verification, Validation, and Uncertainty Quantification
|July 14, 2022
Summary
Uncertainty in cardiovascular model inputs, like flow profiles, impacts predictions. This study uses an ensemble Kalman filter to estimate inflow uncertainty, improving the reliability of area and pressure predictions in arterial models.
Area of Science:
- Cardiovascular dynamics
- Biomedical engineering
- Computational fluid dynamics
Background:
- Patient-specific cardiovascular models are crucial for clinical applications.
- Input uncertainties in 1D arterial network models affect output reliability.
- Uncertainties arise from geometry, fluid/wall parameters, and boundary conditions.
Purpose of the Study:
- To investigate the impact of inlet flow profile uncertainty on 1D cardiovascular model predictions.
- To develop and apply an ensemble Kalman filter (EnKF) for estimating temporal inflow profiles.
- To quantify uncertainty propagation in area and pressure predictions.
Main Methods:
- Developed an iterative EnKF-based scheme to estimate inflow profiles from a prior distribution.
- Propagated inflow uncertainty through a 1D cardiovascular model.
- Validated model predictions against ex vivo measurements in a sheep model.
Main Results:
- The EnKF-based inflow estimator quantified uncertainty in estimated inflow profiles.
- Propagated uncertainty affected area and pressure predictions in the model.
- Model predictions were compared with ex vivo data from the ascending aorta, carotid, and femoral arteries.
Conclusions:
- Estimating inflow uncertainty is vital for reliable cardiovascular model predictions.
- The EnKF approach provides a robust method for quantifying input uncertainty.
- Findings contribute to improving the accuracy of patient-specific cardiovascular modeling.
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