Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation

TL;DR

This paper introduces a real-time mesh refinement method for needle insertion simulations, improving accuracy in displacement and stress calculations crucial for deep brain stimulation and similar procedures.

Contribution

It presents an error-controlled mesh adaptation technique that enhances simulation precision while maintaining computational efficiency for real-time applications.

Findings

Increased accuracy of displacement and stress fields around the needle.

Maintained real-time simulation capability with adaptive mesh refinement.

Applicable to various percutaneous procedures and robotic guidance.

Abstract

We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift. Our approach enables to control the error in the computation of the displacement and stress fields around the needle tip and needle shaft by suitably refining the mesh, whilst maintaining a coarser mesh in other parts of the domain. We demonstrate through academic and practical examples that our approach increases the accuracy of the displacement and stress fields around the needle without increasing the computational expense. This enables real-time simulations. The proposed methodology has direct implications to increase the accuracy and control the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anesthesia, or cryotherapy and can be essential to the development of robotic guidance.