High-fidelity virtual endovascular aneurysm repair model as a decision-making tool.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Mechanics

Background:

• Endovascular aneurysm repair (EVAR) offers short-term benefits, but long-term success depends on effective planning tools.
• Current pre-operative planning relies on CT scans, which don't fully capture vascular changes or device mechanics.

Purpose of the Study:

• To develop a high-fidelity virtual EVAR model for predicting procedural outcomes and optimizing treatment strategies.
• To create a non-invasive planning tool using only patient imaging data.

Main Methods:

• Reconstructed patient vascular anatomy from imaging data.
• Utilized finite element simulations to model stent graft (SG) crimping, device navigation, and SG implantation.
• Continuously tracked tissue deformation and stored energy throughout the simulated procedure.

Main Results:

• Validated the model against post-operative CT data using image fusion.
• Achieved strong alignment between simulated and actual lumen centerlines (mean modified Hausdorff distance: 1.71 ± 1.40 mm).
• Demonstrated low lumen radius error (1.65 ± 1.13 mm), confirming model validity.

Conclusions:

• The developed virtual EVAR model is a high-fidelity, automated, and cost-effective tool.
• This framework can complement current EVAR pre-planning, aiding device selection and navigation.
• Potential to reduce complications and improve patient outcomes in EVAR procedures.