CTA and DSA based computational fluid dynamics models for morphological and hemodynamic assessment of intracranial...

Area of Science:

• Neuroimaging
• Cardiovascular Research
• Medical Engineering

Background:

• Intracranial atherosclerotic stenosis (ICAS) is a leading cause of ischemic stroke.
• Accurate assessment of anatomical and hemodynamic factors is vital for stroke treatment planning.
• Current clinical evaluations often rely solely on luminal stenosis, potentially missing critical hemodynamic information.

Purpose of the Study:

• To compare the consistency and differences of computational fluid dynamics (CFD) models derived from digital subtraction angiography (DSA), computed tomography angiography (CTA), and a hybrid CTA model incorporating DSA hemodynamic data (CMD).
• To evaluate the efficacy of these models in assessing ICAS characteristics.
• To determine the potential of integrating anatomical and hemodynamic data for improved stroke risk stratification.

Main Methods:

• Retrospective analysis of 40 ICAS patients who underwent both CTA and DSA.
• Patient-specific CFD simulations using standardized boundary conditions to assess morphological and hemodynamic parameters (pressure ratio, wall shear stress ratio, high shear stress areas).
• Statistical analyses including paired comparisons, intraclass correlation coefficients (ICC), and Bland-Altman analysis to compare model outputs.

Main Results:

• CTA-based CFD models showed high consistency with DSA for anatomical measurements (ICC > 0.90).
• The CMD approach significantly improved consistency for functional metrics, with CMD-derived pressure ratio (PR) and wall shear stress ratio (WSSR) highly concordant with DSA results.
• CTA alone tended to underestimate WSSR, especially in middle cerebral artery lesions, highlighting the importance of hemodynamic data integration.

Conclusions:

• CTA-based CFD modeling is a dependable, non-invasive alternative to DSA for morphological ICAS assessment.
• The CMD method enhances functional evaluation accuracy by incorporating DSA flow data into CTA models.
• Integrating anatomical imaging with hemodynamic modeling via methods like CMD holds significant promise for improving clinical stroke risk stratification.