Controlling the error on target motion through real-time mesh adaptation: Applications to deep brain stimulation

Area of Science:

• Computational mechanics
• Biomedical engineering
• Medical simulation

Background:

• Needle insertion simulations are crucial for percutaneous procedures and robotic surgery.
• Accurate simulations require managing computational expense, especially with complex phenomena like brain shift.
• Existing methods may lack efficiency in balancing accuracy and computational cost.

Purpose of the Study:

• To present an error-controlled mesh refinement procedure for needle insertion simulations.
• To demonstrate the procedure's effectiveness in improving accuracy and reducing computational time.
• To apply the method to deep brain stimulation electrode implantation, considering brain shift.

Main Methods:

• Developed an adaptive mesh refinement strategy based on error localization around the needle.
• Applied the procedure to simulations of needle insertion, including deep brain stimulation electrode implantation.
• Incorporated the brain shift phenomenon into the simulation model.

Main Results:

• The error in displacement and stress fields is localized around the needle tip and shaft.
• Adaptive mesh refinement significantly reduces error in these critical regions.
• The approach enhances accuracy compared to uniform coarse meshes and saves computational time versus uniform finer meshes, facilitating real-time simulations.

Conclusions:

• The proposed error-controlled mesh refinement procedure effectively enhances simulation accuracy and efficiency.
• This methodology is applicable to various percutaneous procedures and robotic surgery development.
• The ability to control computational expense and maintain accuracy has significant implications for medical simulations.