Patient-specific, touch-based registration during robotic, image-guided partial nephrectomy.

Image-guidance during partial nephrectomy enables navigation within the operative field alongside a 3-dimensional roadmap of renal anatomy generated from patient-specific imaging. Once a process is performed by the human mind, the technology will allow standardization of the task for the benefit of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efficiency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical fields; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrectomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner.

Accuracy of Touch-Based Registration During Robotic Image-Guided Partial Nephrectomy Before and After Tumor Resection in Validated Phantoms.

Aim: Image-guided surgery (IGS) allows for accurate, real-time localization of subsurface critical structures during surgery. No prior IGS systems have described a feasible method of intraoperative reregistration after manipulation of the kidney during robotic partial nephrectomy (PN). We present a method for seamless reregistration during IGS and evaluate accuracy before and after tumor resection in two validated kidney phantoms. Materials and Methods: We performed robotic PN on two validated kidney phantoms-one with an endophytic tumor and one with an exophytic tumor-with our IGS system utilizing the da Vinci Xi robot. Intraoperatively, the kidney phantoms' surfaces were digitized with the da Vinci robotic manipulator via a touch-based method and registered to a three-dimensional segmented model created from cross-sectional CT imaging of the phantoms. Fiducial points were marked with a surgical marking pen and identified after the initial registration using the robotic manipulator. Segmented images were displayed via picture-in-picture in the surgeon console as tumor resection was performed. After resection, reregistration was performed by reidentifying the fiducial points. The accuracy of the initial registration and reregistration was compared. Results: The root mean square (RMS) averages of target registration error (TRE) were 2.53 and 4.88 mm for the endophytic and exophytic phantoms, respectively. IGS enabled resection along preplanned contours. Specifically, the RMS averages of the normal TRE over the entire resection surface were 0.75 and 2.15 mm for the endophytic and exophytic phantoms, respectively. Both tumors were resected with grossly negative margins. Point-based reregistration enabled instantaneous reregistration with minimal impact on RMS TRE compared with the initial registration (from 1.34 to 1.70 mm preresection and from 1.60 to 2.10 mm postresection). Conclusions: We present a novel and accurate registration and reregistration framework for use during IGS for PN with the da Vinci Xi surgical system. The technology is easily integrated into the surgical workflow and does not require additional hardware.

Comparing the accuracy of the da Vinci Xi and da Vinci Si for image guidance and automation.

BACKGROUND: Current laparoscopic surgical robots are teleoperated, which requires high fidelity differential motions but does not require absolute accuracy. Emerging applications, including image guidance and automation, require absolute accuracy. The absolute accuracy of the da Vinci Xi robot has not yet been characterized or compared to the Si system, which is now being phased out. This study compares the accuracy of the two. METHODS: We measure robot tip positions and encoder values assessing accuracy with and without robot calibration. RESULTS: The Si is accurate if the setup joints are not moved but loses accuracy otherwise. The Xi is always accurate. CONCLUSION: The Xi can achieve submillimetric average error. Calibration improves accuracy, but excellent baseline accuracy of the Xi means that calibration may not be needed for some applications. Importantly, the external tracking systems needed to account for setup joint error in the Si are no longer required with the Xi.