Development of decision support tools by model order reduction for active endovascular navigation.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Interventional Neurology

Background:

• Endovascular therapies offer minimally invasive treatment for vascular diseases.
• Navigating complex vascular anatomy, such as the Supra-Aortic Trunks (SATs), remains a significant challenge in endovascular procedures.
• Current patient-specific finite element (FE) simulations for device navigation are computationally intensive, limiting real-time application.

Purpose of the Study:

• To develop a real-time computational method for guiding active guidewires and catheters in complex vascular anatomies.
• To enhance catheterization access to challenging neurovascular targets via the left carotid artery.
• To create predictive numerical charts for active guidewire behavior and navigation.

Main Methods:

• Development of numerical charts based on high-fidelity FE simulations.
• Utilizing the High Order Proper Generalized Decomposition (HOPGD) method for prediction.
• Validation against patient-specific aorta models and simulation of guidewire/catheter navigation.

Main Results:

• Numerical charts enable real-time prediction of active guidewire behavior and navigation.
• The HOPGD method achieves prediction errors below 5% with response times of 10^-3 seconds.
• The approach is based on a limited set of high-fidelity FE computations.

Conclusions:

• The developed numerical charts offer a computationally efficient solution for real-time navigation assistance in endovascular procedures.
• This technology has the potential to significantly improve the safety and efficacy of neurovascular interventions.
• The findings pave the way for clinically applicable methods to enhance complex catheterizations.