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Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle.

J O Campos^1,2, J Sundnes³, R W Dos Santos²

¹Centro Federal de Educação Tecnológica de Minas Gerais, Leopoldina, Brazil.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|May 26, 2020

Summary

Uncertainty quantification in patient-specific cardiac simulations is crucial for reliable clinical use. Key parameters like active stress, wall thickness, and fiber orientation significantly impact left ventricle function predictions.

Keywords:

cardiac mechanics sensitivity analysis uncertainty quantification

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Area of Science:

Computational mechanics
Biomedical engineering
Cardiac modeling

Background:

Patient-specific computer simulations offer potential in clinical diagnostics and treatment development.
Reliability of these cardiac simulations is paramount for practical application.
Model construction involves uncertainties in parameters, geometry, and fiber orientation, often due to semi-manual processes.

Purpose of the Study:

To perform uncertainty quantification and sensitivity analyses on cardiac simulations.
To assess the variability in key quantities of interest (QoI) for left ventricle function.
To identify parameters that significantly contribute to prediction variability.

Main Methods:

Incorporated uncertainties in multiple model parameters: regional wall thickness, fiber orientation, passive material properties, active stress, and circulatory model.
Performed uncertainty quantification and sensitivity analyses on left ventricle function simulations.
Analyzed clinical quantities for overall variability and key contributors.

Main Results:

Quantities of interest (QoI) demonstrated high sensitivity to active stress, wall thickness, and fiber direction.
Ejection fraction and ventricular torsion were identified as the most impacted outputs.
Variability in QoI was significantly influenced by the chosen parameters.

Conclusions:

Improving the precision of cardiac mechanics models requires addressing uncertainties in geometrical reconstruction, active stress estimation, and fiber orientation.
Reducing errors in these semi-manual processes is essential for enhancing model reliability.
Further research into robust uncertainty quantification methods is needed for clinical translation.