An Automated Workflow for Hemodynamic Computations in Cerebral Aneurysms.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Science

Background:

• Computational fluid dynamics (CFD) is crucial for understanding cerebral aneurysm hemodynamics.
• Clinical adoption of CFD is hindered by high computational costs and required expertise.
• Flow-related quantities in aneurysms may influence growth and rupture risk.

Purpose of the Study:

• To develop a practical, robust, and automated CFD methodology for cerebral aneurysm analysis.
• To minimize user interaction and computational expense in CFD simulations.
• To enhance the clinical applicability of hemodynamic simulations.

Main Methods:

• A fully automated pipeline integrating patient-specific models with a GPU-accelerated CFD solver.
• Utilized a reduced order model for initial spatial/temporal resolution estimation.
• Implemented an iterative approach for dynamic resolution adjustment during simulation.

Main Results:

• Validated pipeline against published data and a commercial solver (Ansys CFX).
• Achieved close agreement with state-of-the-art results (average 2% relative difference).
• Demonstrated computational efficiency with median simulation times of 40 minutes.

Conclusions:

• The automated CFD pipeline significantly enhances computational performance and reduces user interaction.
• The methodology provides accurate hemodynamic insights for cerebral aneurysms.
• This approach facilitates wider clinical adoption of CFD for aneurysm assessment.