We are working to help surgeons see invisible subsurface anatomy during surgery - even before they make the first cut.

This is particularly important in the kidney, because the surgeon must cut in exactly the right place. Cut too close to the (subsurface, unseen) tumor and the surgeon might accidentally leave part of the tumor behind to re-grow. Cut too far away from the tumor and valuable healthy kidney tissue that the patient will rely on for the rest of their life has been removed.

We are using image guidance to help. We give the surgeon a real-time heads-up display of 3D models made from pre-operative images during surgery. Importantly, we accurately align these models with the patient’s anatomy and show the surgeon exactly where his/her tools are. We do this by using the da Vinci robot as a digitization tool - the surgeon uses the da Vinci tool tips to lightly touch the tissue and we collect a cloud of surface points, to which we can register image data.

We regularly test this system in humans at Vanderbilt Medical Center. We do this through a unique, inherently safe, strategy that partitions information by using two surgeons - one who performs the surgery according to the standard of care (and never sees our image guidance display), and another who periodically takes over and points at or touches objects to collect data.

The video below talks about image guidance in general and describes point collection and registration. The only difference is we use a unique, optically tracked distance measurement device in place of the da Vinci robot, which makes the demo more portable and easier to show at conferences and other locations outside the operating room where a da Vinci may not be available.

Selected Related Publications:

1. J. M. Ferguson, E. B. Pitt, A. A. Remirez, M. A. Siebold, A. Kuntz, N. L. Kavoussi, E. J. Barth, S.D. Herrell, III, and R. J. Webster, III. Toward Practical and Accurate Touch-Based Image Guidance for Robotic Partial Nephrectomy. IEEE Transactions on Medical Robotics and Bionics, 2(2):196-205, 2020.

2. J. M. Ferguson, E. B. Pitt, A. Kuntz, J. Granna, N. L. Kavoussi, N. Nimmagadda, E. J. Barth, S. Duke Herrell, III, and R. J. Webster, III. Comparing the Accuracy of the da Vinci Xi and da Vinci Si for Image Guidance and Automation. The International Journal of Medical Robotics and Computer Assisted Surgery, 16(6):1-10 e2149, 2020.

3. N. Nimmagadda, J. M. Ferguson, N. L. Kavoussi, B. Pitt, E. J. Barth, J. Granna, R. J. Webster III, and D. S. Herrell III. Patient-Specific, Touch-Based Registration During Robotic, Image-Guided Partial Nephrectomy. World Journal of Urology, 40(3):671–677, 2022.

4. P. C. Cannon, J. M. Ferguson, B. E. Pitt, J. A. Shrand, S. A. Setia, N. Nimmagadda, E. J. Barth, N. L. Kavoussi, R. L. Galloway, S. D. Herrell III, and R. J. Webster III. A Safe Framework for Quantitative In Vivo Human Evaluation of Image Guidance. IEEE Open Journal of Engineering in Medicine and Biology, 5:133-139, 2023.

5. E. B. Pitt, J. M. Ferguson, N. L. Kavoussi, E. J. Barth, R. J. Webster, III, and S. D. Herrell, “Intraoperative Guidance for Robotic Partial Nephrectomy Using Surface-Based Registration: Initial Model Assessment”, Engineering & Eurology Society Annual Meeting, 2019. Best Paper Award.