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Backflow length predictions during flow-controlled infusions using a nonlinear biphasic finite element model.

Gustavo A Orozco1, Joshua H Smith, José J García

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Summary

A revised model predicts brain infusion backflow length. For soft tissues, lower stiffness reduces backflow by increasing fluid transfer, unlike stiffer tissues.

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Area of Science:

  • Biomedical Engineering
  • Computational Mechanics
  • Neuroscience

Background:

  • Accurate prediction of fluid dynamics during brain infusions is critical for treatment efficacy and safety.
  • Existing models often simplify material properties and boundary conditions, potentially leading to inaccurate backflow predictions.

Purpose of the Study:

  • To revise a finite element model (FEM) incorporating geometric and material nonlinearities to predict backflow length during brain tissue infusions.
  • To develop a power-law formula based on the revised FEM to estimate backflow length and analyze the influence of material properties and catheter geometry.

Main Methods:

  • Revision of a previously proposed FEM to include nonlinearities and free boundary conditions at the catheter tip and lateral surface.
  • Fitting a power-law formula to FEM results to predict backflow length.
  • Comparison of power-law predictions with a closed-form solution based on linear elasticity.

Main Results:

  • The power-law formula predicted a lower influence of shear modulus and catheter radius on backflow length for compliant materials compared to linear elasticity models.
  • For stiffer materials, the power-law formula's predictions were consistent with the linear elasticity solution.
  • FEM showed that reducing shear modulus in highly compliant materials (<500 Pa) decreased backflow length due to increased infusion area and reduced hydraulic resistance.

Conclusions:

  • Material and geometric nonlinearities near the infusion surface significantly impact backflow length during brain infusions.
  • Hydraulic conductivity changes with strain, necessitating consideration in models for accurate characterization of backflow.
  • The revised FEM and power-law formula provide a more accurate approach to predicting backflow length, especially for infusions into soft brain tissue.