Toward image-guided partial nephrectomy with the da Vinci robot: exploring surface acquisition methods for intraoperative re-registration

James M. Ferguson; Leon Y. Cai; Alexander Reed; Michael Siebold; Smita De; S. Duke Herrell; Robert J. Webster

doi:10.1117/12.2296464

12 March 2018 Toward image-guided partial nephrectomy with the da Vinci robot: exploring surface acquisition methods for intraoperative re-registration

James M. Ferguson, Leon Y. Cai, Alexander Reed, Michael Siebold, Smita De, S. Duke Herrell, Robert J. Webster

Author Affiliations +

Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 1057609 (2018) https://doi.org/10.1117/12.2296464
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Our overarching goal is to facilitate wider adoption of robot-assisted partial nephrectomy through image- guidance, which can enable a surgeon to visualize subsurface features and instrument locations in real time intraoperatively. This is motivated by the observation that while there are compelling lifelong health benefits of partial nephrectomy, radical nephrectomy remains an overused surgical approach for many kidney cancers. Image-guidance may facilitate wider adoption of the procedure because it has the potential to increase surgeons' confidence in efficiently and safely exposing critical structures as well as achieving negative margins with maximal benign tissue sparing, particularly in a minimally invasive setting. To maintain the accuracy of image-guidance during the procedure as the kidney moves, periodic re-registration of medical image data to kidney anatomy is necessary. In this paper, we evaluate three registration approaches for the da Vinci Surgical System that have the potential to enable real-time updates to the display of segmented preoperative images within its console. Specifically, we compare the use of surface ink fiducials triangulated from stereo endoscope images, point clouds obtained without fiducials using a stereoscopic depth mapping algorithm, and points obtained by lightly tracing the da Vinci tool tip over the kidney surface. We compare and contrast the three approaches from both an accuracy and a workflow perspective.

Conference Presentation

Citation Download Citation

James M. Ferguson, Leon Y. Cai, Alexander Reed, Michael Siebold, Smita De, S. Duke Herrell, and Robert J. Webster "Toward image-guided partial nephrectomy with the da Vinci robot: exploring surface acquisition methods for intraoperative re-registration", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057609 (12 March 2018); https://doi.org/10.1117/12.2296464

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