An automatic CFD-based flow diverter optimization principle for patient-specific intracranial aneurysms.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Fluid Dynamics

Background:

• Intracranial aneurysms pose treatment challenges due to complex anatomy.
• Flow diverting devices are used, but optimal stent selection and deployment are difficult.

Purpose of the Study:

• To develop and validate a computational method for optimizing flow diverter deployment in intracranial aneurysms.
• To support personalized therapy planning for aneurysm treatment.

Main Methods:

• A 3D CFD-based optimization combined with realistic virtual stent deployment.
• Automated simulation loop considering patient-specific aneurysm geometry and varying stent compressions.
• Application to a patient-specific giant intracranial aneurysm.

Main Results:

• Virtual deployment of an uncompressed flow diverter reduced aneurysm inflow by 24.4%.
• Local stent compression varied blood flow reduction between 27.3% and 33.4%.
• Identified a broad range of potential treatment outcomes, highlighting deployment variability.

Conclusions:

• The developed method is a proof of concept for automatically identifying optimal virtual stenting strategies.
• This approach can significantly aid physicians in future intracranial aneurysm therapy planning.