Megha Kalia1,2, Prateek Mathur3, Keith Tsang4, Peter Black5, Nassir Navab6, Septimiu Salcudean4. 1. Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver, BC, V6T 1Z4, Canada. mkalia@ece.ubc.ca. 2. Computer Aided Medical Procedures, Technical University of Munich, Boltzmannstraße 15, 85748, Garching bei München, Germany. mkalia@ece.ubc.ca. 3. Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver, BC, V6T 1Z4, Canada. pmathur@ece.ubc.ca. 4. Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver, BC, V6T 1Z4, Canada. 5. Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, V5Z 1M9, Canada. 6. Computer Aided Medical Procedures, Technical University of Munich, Boltzmannstraße 15, 85748, Garching bei München, Germany.
Abstract
PURPOSE: Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot is a common treatment for organ-confined prostate cancer. Augmented reality (AR) can help during RALRP by showing the surgeon the location of anatomical structures and tumors from preoperative imaging. Previously, we proposed hand-eye and camera intrinsic matrix estimation procedures that can be carried out with conventional instruments within the patient during surgery, take < 3 min to perform, and fit seamlessly in the existing surgical workflow. In this paper, we describe and evaluate a complete AR guidance system for RALRP and quantify its accuracy. METHODS: Our AR system requires three transformations: the transrectal ultrasound (TRUS) to da Vinci transformation, the camera intrinsic matrix, and the hand-eye transformation. For evaluation, a 3D-printed cross-wire was visualized in TRUS and stereo endoscope in a water bath. Manually triangulated cross-wire points from stereo images were used as ground truth to evaluate overall TRE between these points and points transformed from TRUS to camera. RESULTS: After transforming the ground-truth points from the TRUS to the camera coordinate frame, the mean target registration error (TRE) (SD) was [Formula: see text] mm. The mean TREs (SD) in the x-, y-, and z-directions are [Formula: see text] mm, [Formula: see text] mm, and [Formula: see text] mm, respectively. CONCLUSIONS: We describe and evaluate a complete AR guidance system for RALRP which can augment preoperative data to endoscope camera image, after a deformable magnetic resonance image to TRUS registration step. The streamlined procedures with current surgical workflow and low TRE demonstrate the compatibility and readiness of the system for clinical translation. A detailed sensitivity study remains part of future work.
PURPOSE: Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot is a common treatment for organ-confined prostate cancer. Augmented reality (AR) can help during RALRP by showing the surgeon the location of anatomical structures and tumors from preoperative imaging. Previously, we proposed hand-eye and camera intrinsic matrix estimation procedures that can be carried out with conventional instruments within the patient during surgery, take < 3 min to perform, and fit seamlessly in the existing surgical workflow. In this paper, we describe and evaluate a complete AR guidance system for RALRP and quantify its accuracy. METHODS: Our AR system requires three transformations: the transrectal ultrasound (TRUS) to da Vinci transformation, the camera intrinsic matrix, and the hand-eye transformation. For evaluation, a 3D-printed cross-wire was visualized in TRUS and stereo endoscope in a water bath. Manually triangulated cross-wire points from stereo images were used as ground truth to evaluate overall TRE between these points and points transformed from TRUS to camera. RESULTS: After transforming the ground-truth points from the TRUS to the camera coordinate frame, the mean target registration error (TRE) (SD) was [Formula: see text] mm. The mean TREs (SD) in the x-, y-, and z-directions are [Formula: see text] mm, [Formula: see text] mm, and [Formula: see text] mm, respectively. CONCLUSIONS: We describe and evaluate a complete AR guidance system for RALRP which can augment preoperative data to endoscope camera image, after a deformable magnetic resonance image to TRUS registration step. The streamlined procedures with current surgical workflow and low TRE demonstrate the compatibility and readiness of the system for clinical translation. A detailed sensitivity study remains part of future work.
Entities:
Keywords:
Augmented reality; Image guided surgery; Medical imaging; Prostate surgery; Robotic surgery