Clinical correlation of hemodynamic parameters using blood flow prediction model in cerebrovascular arteries

Patient-specific hemodynamic assessment is pivotal for optimizing treatment strategies in cerebrovascular interventions; however, conventional modalities are often limited by restricted clinical accessibility or prolonged acquisition times.

This study evaluates the previously developed Dr. NEAR flow (DNF), a streamlined computational framework designed for rapid and clinically feasible blood flow prediction, validated against Non-invasive Optimal Vessel Analysis (NOVA), a quantitative magnetic resonance angiography technique. The DNF methodology reconstructs a one-dimensional vascular topology from standard time-of-flight magnetic resonance angiography (MRA-TOF) sequences, and flow rate, velocity, and relative pressure are computed throughout the reconstructed vascular network using a Poiseuille-based fluid model. For validation, patient-specific boundary conditions were parameterized using proximal inflow measurements obtained from NOVA, and quantitative comparisons were performed at arterial locations with corresponding NOVA measurements.

Across 230 arterial segments in 35 subjects, NOVA-measured and DNF-predicted flow rates were strongly correlated (r = 0.77), and the association remained significant after accounting for within-patient clustering (β = 0.863, 95% CI 0.769-0.957). Repeated-measures Bland-Altman analysis showed a mean bias (-58.04 mm³/s). After conversion of flow rates to velocities, vessel-averaged distributions across the major cerebral arteries showed generally similar patterns between the two modalities, although artery-specific correlations were variable.

These findings indicate that DNF is a feasible approach for cerebrovascular hemodynamic assessment.