Computational Fluid Dynamics in Intracranial Atherosclerotic Disease

Area of Science:

• Neurology
• Biomedical Engineering
• Medical Imaging

Background:

• Intracranial atherosclerotic disease (ICAD) carries a high risk of recurrent stroke, even with optimal medical management.
• Current imaging methods often fail to fully capture the intricate hemodynamics of focal stenosis, limiting stroke risk prediction.
• Hemodynamic insufficiency is a known risk factor, but its precise assessment in ICAD remains challenging.

Purpose of the Study:

• To review the existing literature on the application of computational fluid dynamics (CFD) in managing intracranial atherosclerotic disease.
• To explore the potential of CFD in improving risk stratification and guiding targeted therapies for ICAD patients.
• To highlight the limitations of unimodal imaging in assessing complex hemodynamic environments in ICAD.

Main Methods:

• Literature review of studies employing computational fluid dynamics (CFD) for intracranial atherosclerotic disease (ICAD).
• Analysis of how CFD simulates blood flow dynamics and hemodynamic forces around atherosclerotic lesions.
• Evaluation of CFD's role in patient risk stratification based on simulated hemodynamic outputs.

Main Results:

• CFD offers a multimodal approach to visualize and analyze the complex hemodynamic environment in ICAD.
• Simulated hemodynamic parameters derived from CFD can potentially identify patients at higher risk for recurrent stroke.
• Existing studies demonstrate CFD's capability to illustrate hemodynamic consequences of focal stenosis in cerebral arteries.

Conclusions:

• Computational fluid dynamics (CFD) shows promise as a tool to better understand and manage stroke risk in intracranial atherosclerotic disease (ICAD).
• CFD-derived hemodynamic insights may enable more precise risk stratification than traditional methods.
• Future research utilizing CFD could pave the way for developing highly targeted and effective therapies for ICAD.