A computationally efficient stochastic analysis method for predicting the long-term development of aneurysms.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging

Background:

• Aneurysm progression and rupture mechanisms are poorly understood due to patient-specific variations and complex vascular tissue dynamics.
• Existing models often struggle to accurately capture the long-term behavior and inherent uncertainties in aneurysm development.

Purpose of the Study:

• To develop a novel hybrid computational framework for simulating aneurysm progression and rupture.
• To integrate uncertainty quantification with biomechanical modeling for enhanced predictive accuracy.
• To significantly reduce computational time compared to traditional methods.

Main Methods:

• A hybrid framework combining an efficient healing model for aneurysm progression simulation.
• Development of a reduced-order stochastic solver using Legendre polynomial surrogate models and back-propagation artificial neural networks.
• Optimization of parameter sampling through the Smolyak algorithm.

Main Results:

• The proposed framework accurately simulates aneurysm progression and rupture mechanisms.
• Achieved a 90% reduction in computational time compared to standard Monte Carlo methods.
• Demonstrated high accuracy in predictions despite significant computational efficiency gains.

Conclusions:

• The novel hybrid computational framework offers a powerful tool for understanding aneurysm dynamics.
• This approach enhances the speed and accuracy of simulating complex biological processes.
• The methodology holds potential for improving patient-specific risk assessment and treatment planning for aneurysms.