A Multi-Camera Optical Tag Neuronavigation and AR Augmentation Framework for Non-Invasive Brain Stimulation

TL;DR

This paper introduces a real-time, computer-vision-based neuronavigation system using multi-camera optical tracking and AR to improve the accuracy and usability of non-invasive brain stimulation procedures.

Contribution

It presents a novel multi-camera optical tracking and AR framework that enhances neuronavigation for TMS with real-time visualization and interaction capabilities.

Findings

High spatial precision and accuracy demonstrated

System is user-friendly in a clinical case study

Real-time visualization improves clinician interaction

Abstract

Accurate neuronavigation is essential for generating the intended effect with transcranial magnetic stimulation (TMS). Precise coil placement also directly influences stimulation efficacy. Traditional neuronavigation systems often rely on costly and still hard to use and error-prone tracking systems. To solve these limitations, we present a computer-vision-based neuronavigation system for real-time tracking of patient and TMS instrumentation. The system can feed the necessary data for a digital twin to track TMS stimulation targets. We integrate a self-coordinating optical tracking system with multiple consumer-grade cameras and visible tags with a dynamic 3D brain model in Unity. This model updates in real time to represent the current stimulation coil position and the estimated stimulation point to intuitively visualize neural targets for clinicians. We incorporate an augmented reality (AR) module to bridge the gap between the visualization of the digital twin and the real world and project the brain model in real-time onto the head of a patient. AR headsets or mobile AR devices allow clinicians to interactively view and adjust the placement of the stimulation transducer intuitively instead of guidance through abstract numbers and 6D cross hairs on an external screen. The proposed technique provides improved spatial precision as well as accuracy. A case study with ten participants with a medical background also demonstrates that the system has high usability.