While intraoperative imaging is commonly used to guide surgical interventions, automatic robotic support for image-guided navigation has not yet been established in clinical routine. In this paper, we propose a novel visual servoing framework that combines, for the first time, full image-based 3D ultrasound registration with a real-time servo-control scheme. Paired with multi-modal fusion to a pre-interventional plan such as an annotated needle insertion path, it thus allows tracking a target anatomy, continuously updating the plan as the target moves, and keeping a needle guide aligned for accurate manual insertion. The presented system includes a motorized 3D ultrasound transducer mounted on a force-controlled robot and a GPU-based image processing toolkit. The tracking accuracy of our framework is validated on a geometric agar/gelatin phantom using a second robot, achieving positioning errors of on average 0.42+/-0.44 mm. With compounding and registration runtimes of up to total around 550 ms, real-time performance comes into reach. We also present initial results on a spine phantom, demonstrating the feasibility of our system for lumbar spine injections.
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While intraoperative imaging is commonly used to guide surgical interventions, automatic robotic support for image-guided navigation has not yet been established in clinical routine. In this paper, we propose a novel visual servoing framework that combines, for the first time, full image-based 3D ultrasound registration with a real-time servo-control scheme. Paired with multi-modal fusion to a pre-interventional plan such as an annotated needle insertion path, it thus allows tracking a target a...
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