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

Introduction: 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. Methods: 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. Results: Across 230 arterial segments in 35 subjects, NOVA-measured and DNF-predicted flow rates were strongly correlated ( Discussion: These findings indicate that DNF is a feasible approach for cerebrovascular hemodynamic assessment.