Neural network-based pose estimation and real-time tracking of ultrasound probes

Object tracking is a crucial component in the medical field, with great potential to enhance clinical workflows, particularly when integrated with augmented reality technologies. Accurate and reliable tracking systems can improve precision, usability, and operator feedback, facilitating innovative applications such as telemedicine, medical training, and robot-assisted procedures. This study evaluates the performance of FoundationPose, a neural network designed for six-degree-of-freedom pose estimation and real-time object tracking, in the context of ultrasound probe tracking. The RGB and depth images necessary for the network's operation were acquired using an Intel RealSense D435 3D camera. The feasibility and accuracy of FoundationPose were evaluated by analysing its ability to estimate both the translational and rotational components of the probe's pose. Experimental results demonstrated the network's ability to achieve mean errors of less than 6mm in distance estimation and under 1° in rotation tracking, with low sensitivity to the initialisation point. These findings confirm the potential of FoundationPose for real-time ultrasound probe tracking in controlled conditions. Future developments could focus on integrating this system with augmented reality platforms to provide real-time visual guidance and enhance clinical applications.Clinical relevance-This system enables precise real-time ultrasound probe tracking, enhancing procedural accuracy and supporting advanced clinical applications like augmented reality guidance and robotic-assisted interventions.