Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces

Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the tendon-driven, compliant robot structure. As a result, the adequate orientation of the probe towards the tissue surface remains unknown and the anatomy reconstructed from scan may become highly inaccurate. In this work we present solutions to both of these challenges: an attitude sensor fusion scheme for improved kinematic sensing and a visual, deep learning based algorithm to establish and maintain contact between the organ surface and the US probe. We further introduce a novel scheme to estimate and orient the probe perpendicular to the center line of a vascular structure. Our approach enables, for the first time, to autonomously scan across a non-planar surface and navigate along an anatomical structure with a robotically guided minimally invasive US probe. Our experiments on a vessel phantom with a convex surface confirm a significant improvement of the reconstructed curved vessel geometry, with our approach strongly reducing the mean positional error and variance. In the future, our approach could help identify vascular structures more effectively and help pave the way towards semi-autonomous assistance during partial hepatectomy and the potential to reduce procedure length and complication rates.

Automated summary

This study presents solutions to the challenges of inaccurate robot motion and probe positioning in ultrasound scanning through attitude sensing and deep learning algorithms, and successfully achieves autonomous scanning and navigation. The experiments demonstrate a significant improvement in the reconstructed curved vessel geometry.