A fast prediction method for pre-puncture brain deformation based on a mass-spring-damper and potential field model

Area of Science:

• Biomechanics
• Computational modeling
• Surgical simulation

Background:

• Accurate prediction of meningeal deformation and puncture force is crucial for surgical planning.
• Existing methods like the finite element method are computationally intensive.
• Need for faster simulation tools for real-time surgical applications.

Purpose of the Study:

• To develop and validate a reduced-order hybrid model for rapid prediction of meningeal deformation and puncture force.
• To enable efficient surgical simulation and planning.
• To investigate compression-induced deformation and tissue resistance.

Main Methods:

• A hybrid model combining mass-spring-damper framework and potential field method.
• Simplification of the brain into meningeal, cerebrospinal fluid, and tissue layers.
• Implementation in MATLAB and validation with Abaqus simulations and phantom experiments.

Main Results:

• The model accurately predicted deformation and puncture force at a 5 mm puncture depth.
• Achieved simulation speeds over two orders of magnitude faster than the finite element method.
• Demonstrated efficient simulation of puncture-induced tissue deformation.

Conclusions:

• The proposed reduced-order hybrid model offers a computationally efficient solution for surgical simulation.
• Enables rapid prediction of key mechanical responses during surgical procedures.
• Facilitates improved surgical planning and understanding of brain tissue mechanics.