Computational Hemodynamics Framework for the Analysis of Cerebral Aneurysms

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Fluid Dynamics

Background:

• Assessing intracranial aneurysm rupture risk is crucial as treatment risks can outweigh natural rupture risks.
• Existing image-based computational fluid dynamics (CFD) models lack consensus on key hemodynamic predictors.
• A computational framework is needed to link hemodynamic characteristics to aneurysm clinical events.

Purpose of the Study:

• To develop and apply a computational framework for characterizing cerebral aneurysm hemodynamics.
• To identify specific hemodynamic quantities indicative of aneurysm growth or rupture.
• To relate hemodynamic environments to clinical outcomes like aneurysm rupture.

Main Methods:

• Utilized image-based computational fluid dynamics (CFD) models to extract patient-specific hemodynamics.
• Proposed novel hemodynamic quantities to describe salient features of the aneurysm environment.
• Applied the framework to a patient population to analyze hemodynamic differences between ruptured and unruptured aneurysms.

Main Results:

• Ruptured aneurysms exhibited concentrated inflows and concentrated wall shear stress distributions.
• High maximal wall shear stress was a characteristic of ruptured aneurysms.
• Ruptured aneurysms showed smaller viscous dissipation ratios compared to unruptured ones.
• These statistical associations remained consistent across different physiologic flow conditions.

Conclusions:

• Hemodynamic information from CFD models can effectively assess intracranial aneurysm rupture risk.
• The framework aids in testing hypotheses regarding aneurysm formation, progression, and rupture mechanisms.
• This approach can provide answers to specific clinical questions regarding aneurysm management.