Electro-localization method using a muscle conductive phantom for needle position detection towards medical training.

Area of Science:

• Biomedical Engineering
• Medical Simulation
• Sensor Technology

Background:

• Healthcare simulation requires accurate tracking of instruments like needles for training, particularly for procedures such as needle electromyography (nEMG).
• Current methods using external cameras for needle localization face challenges with error sources, necessitating advanced internal sensing technologies.
• Real-time estimation of needle position is crucial for effective haptic feedback and performance evaluation in medical training simulators.

Purpose of the Study:

• To introduce and demonstrate the feasibility of a conductive phantom for sensing 3D needle position during insertion.
• To develop a novel electro-localization technique integrated within a muscle phantom for medical training.
• To evaluate the accuracy of the proposed system for real-time needle tracking in a simulated nEMG environment.

Main Methods:

• A conductive muscle phantom was designed with surface electrodes to generate varying voltage distributions.
• Needle insertion into the phantom allowed for voltage measurements, which were then mapped to spatial coordinates.
• A finite element method (FEM)-based computational model was employed to process voltage data and estimate the 3D needle tip position.

Main Results:

• Experimental and simulation results for phantom voltage distributions showed strong agreement.
• 2D needle position mapping achieved an error of 1.7 mm, reduced to 1.5 mm in the central phantom area.
• 3D needle position mapping demonstrated an error of 4 mm, validating the electro-localization approach.

Conclusions:

• The conductive phantom successfully demonstrated the feasibility of embedded sensing for needle position estimation.
• Electro-localization within the phantom provides a viable alternative to external tracking systems for medical training.
• This technology offers a new pathway for enhancing the realism and effectiveness of needle insertion simulators, particularly for nEMG.