A Biphasic Fluid-Structure Interaction Model of Backflow During Infusion Into Agarose Gel.

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Neuroscience

Background:

• Convection-enhanced delivery (CED) for central nervous system (CNS) disorders is hindered by infusate backflow along the catheter surface.
• Finite element modeling (FEM) is crucial for understanding the physics of backflow and improving CED protocols.
• Previous models, like García et al. (2013), incorporated annular flow physics but lacked advanced simulation capabilities.

Purpose of the Study:

• To generalize and validate a finite element model for simulating infusate backflow during brain tissue infusion.
• To utilize recently developed fluid-finite strain-infusate (FSI) and biphasic-FSI elements in the febio software.
• To assess the impact of catheter size on backflow dynamics and fluid pressure.

Main Methods:

• Developed a generalized finite element model using fluid-FSI and biphasic-FSI elements in the febio software.
• Modeled a catheter with a 0.98 mm radius and simulated infusion into a brain tissue surrogate.
• Validated the model against experimental data for backflow length and maximum fluid pressure, and compared with García et al. (2013).

Main Results:

• The biphasic-FSI model accurately reproduced experimental backflow lengths and maximum fluid pressures.
• Model results showed good agreement with the previous finite element model by García et al. (2013).
• Simulations indicated comparable backflow length and forward flow volume for different catheter sizes, with higher pressure for smaller catheters.

Conclusions:

• The generalized biphasic-FSI model effectively simulates backflow during infusion into brain tissue surrogates.
• The model's accuracy supports its use in refining CED treatment protocols and understanding infusion physics.
• The model has potential for extension to stepped catheter geometries to actively control backflow in CNS treatments.