S1 Table: Computational Fluid Dynamics Results for Intracranial Stenosis Models

A 25.8 KB dataset from figshare, last updated 2026-05-28, containing results from computational fluid dynamics simulations of intracranial atherosclerotic stenosis. The study by Lei Zhengyao modeled middle cerebral artery stenosis at four severity levels under three blood viscosity states to analyze hemodynamic metrics. It includes data on velocity, pressure, wall shear stress, oscillatory shear index, and time-averaged wall shear stress.

Use Cases

• Training machine learning models to predict hemodynamic risk based on stenosis geometry and viscosity parameters.
• Validating computational fluid dynamics simulations of non-Newtonian blood flow in stenotic vessels.
• Analyzing the relationship between stenosis severity and oscillatory shear index for risk stratification.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Simulations include four distinct stenosis severities (30%, 50%, 70%, 90%) and three viscosity states for controlled comparison.

Limitations

• Row count and column-level documentation are unknown, requiring manual inspection after download.
• The dataset's small size (25.8 KB) indicates a limited scope, likely containing summary results rather than raw simulation data.

Provenance

Source

figshare

Collection Method

Generated via computational fluid dynamics simulations on a reconstructed middle cerebral artery stenosis model and idealized variants.

Freshness

Last updated 2026-05-28 17:35:52; freshness should be verified.