Personalized Computational Fluid Dynamics Analysis of Cerebral Venous Hemodynamics in a Case of Deep Cerebral Vein Thrombosis

Background/Objectives: Deep cerebral vein thrombosis (DCVT) is a rare cerebrovascular condition that can result in absence of major venous sinuses. This study uses patient-specific computational fluid dynamics (CFD) to quantify hemodynamic changes in acquired DCVT, focusing on venous outflow redistribution, pressure, and wall shear stress (WSS). Methods: Three-dimensional models of cerebral venous sinuses were reconstructed from magnetic resonance venography (MRV) for a DCVT patient and normal control. Steady-state CFD simulations used physiological inflows with laminar flow assumptions. Sensitivity analyses tested hyperemic conditions and blood rheology effects. Results: In normal anatomy, flow split 70% through superior sagittal sinus and 30% through straight sinus. In DCVT, all flow was rerouted through the superior sagittal sinus. Surprisingly, pressure drop was lower in DCVT (0.67 mmHg vs. 1.3 mmHg in normal). WSS increased moderately in the DCVT superior sagittal sinus (~2.5 Pa peak) but remained within physiological ranges. Under hyperemic conditions, pressures and WSS stayed below pathological thresholds. Conclusions: DCVT redirects venous outflow without pathological pressure or WSS elevations, demonstrating remarkable venous system resilience through collateral compensation. This patient-specific CFD framework enables individualized hemodynamic assessment, contributing to personalized medicine approaches for rare cerebrovascular conditions.