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Personalized Computational Fluid Dynamics Analysis of Cerebral Venous Hemodynamics in a Case of Deep Cerebral Vein
Adisu Mengesha Assefa1, Lina Palaiodimou2, George Bourantas3
1Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece.
Insights
Deep cerebral vein thrombosis (DCVT) reroutes blood flow effectively, showing the venous system
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Imaging
Background:
- Deep cerebral vein thrombosis (DCVT) is a rare cerebrovascular condition.
- DCVT can lead to the absence of major venous sinuses.
- Understanding hemodynamic changes in DCVT is crucial for diagnosis and treatment.
Purpose of the Study:
- To quantify hemodynamic changes in acquired DCVT using patient-specific computational fluid dynamics (CFD).
- To focus on venous outflow redistribution, pressure, and wall shear stress (WSS) in DCVT.
- To compare hemodynamic parameters in DCVT patients with normal controls.
Main Methods:
- Reconstructed 3D cerebral venous sinus models from magnetic resonance venography (MRV).
- Performed steady-state CFD simulations with physiological inflows and laminar flow assumptions.
- Conducted sensitivity analyses for hyperemic conditions and blood rheology.
Main Results:
- In DCVT, all venous outflow was rerouted through the superior sagittal sinus.
- Pressure drop was unexpectedly lower in DCVT compared to normal anatomy (0.67 mmHg vs. 1.3 mmHg).
- Wall shear stress (WSS) in the DCVT superior sagittal sinus remained within physiological ranges, even under hyperemic conditions.
Conclusions:
- DCVT redirects venous outflow without causing pathological pressure or WSS elevations.
- The cerebral venous system demonstrates remarkable resilience through collateral compensation in DCVT.
- Patient-specific CFD provides a framework for individualized hemodynamic assessment in rare cerebrovascular conditions, supporting personalized medicine.
Abstract:
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.
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