Camera-Robot Calibration for the da Vinci® Robotic Surgery System

Long-term oncological outcomes of 3D versus 2D laparoscopic gastrectomy for gastric cancer: a randomized clinical trial

BACKGROUND

Laparoscopy-assisted gastrectomy (LG) is rapidly gaining popularity owing to its minimal invasiveness. Previous studies have found that compared with two-dimensional (2D)-LG, three-dimensional (3D)-LG showed better short-term outcomes. However, the long-term oncological outcomes in patients with locally resectable gastric cancer (GC) remain controversial.

METHODS

In this noninferiority, open-label, randomized clinical trial, a total of 438 eligible GC participants were randomly assigned in a 1:1 ratio to either 3D-LG or 2D-LG from January 2015 to April 2016. The primary endpoint was operating time, while the secondary endpoints included 5-year overall survival (OS), disease-free survival (DFS), and recurrence pattern.

RESULTS

Data from 401 participants were included in the per-protocol analysis, with 204 patients in the 3D group and 197 patients in the 2D group. The 5-year OS and DFS rates were comparable between the 3D and 2D groups (5-year OS: 70.6% vs. 71.1%, Log-rank P = 0.743; 5-year DFS: 68.1% vs. 69.0%, log-rank P = 0.712). No significant differences were observed between the 3D and 2D groups in the 5-year recurrence rate (28.9% vs. 28.9%, P = 0.958) or recurrence time (mean time, 22.6 vs. 20.5 months, P = 0.412). Further stratified analysis based on the type of gastrectomy, postoperative pathological staging, and preoperative BMI showed that the 5-year OS, DFS, and recurrence rates of the 3D group in each subgroup were similar to those of the 2D group (all P > 0.05).

CONCLUSIONS

For patients with locally resectable GC, 3D-LG performed by experienced surgeons in high-volume professional institutions can achieve long-term oncological outcomes comparable to those of 2D-LG.

REGISTRATION NUMBER

NCT02327481 ( http://clinicaltrials.gov ).

A Novel Point Set Registration-Based Hand-Eye Calibration Method for Robot-Assisted Surgery

Pedicle screw insertion with robot assistance dramatically improves surgical accuracy and safety when compared with manual implantation. In developing such a system, hand-eye calibration is an essential component that aims to determine the transformation between a position tracking and robot-arm systems. In this paper, we propose an effective hand-eye calibration method, namely registration-based hand-eye calibration (RHC), which estimates the calibration transformation via point set registration without the need to solve the AX=XB equation. Our hand-eye calibration method consists of tool-tip pivot calibrations in two-coordinate systems, in addition to paired-point matching, where the point pairs are generated via the steady movement of the robot arm in space. After calibration, our system allows for robot-assisted, image-guided pedicle screw insertion. Comprehensive experiments are conducted to verify the efficacy of the proposed hand-eye calibration method. A mean distance deviation of 0.70 mm and a mean angular deviation of 0.68° are achieved by our system when the proposed hand-eye calibration method is used. Further experiments on drilling trajectories are conducted on plastic vertebrae as well as pig vertebrae. A mean distance deviation of 1.01 mm and a mean angular deviation of 1.11° are observed when the drilled trajectories are compared with the planned trajectories on the pig vertebrae.

Optimal robot-world and hand-eye calibration with rotation and translation coupling

A classic hand-eye system involves hand-eye calibration and robot-world and hand-eye calibration. Insofar as hand-eye calibration can solve only hand-eye transformation, this study aims to determine the robot-world and hand-eye transformations simultaneously based on the robot-world and hand-eye equation. According to whether the rotation part and the translation part of the equation are decoupled, the methods can be divided into separable solutions and simultaneous solutions. The separable solutions solve the rotation part before solving the translation part, so the estimated errors of the rotation will be transferred to the translation. In this study, a method was proposed for calculation with rotation and translation coupling; a closed-form solution based on Kronecker product and an iterative solution based on the Gauss–Newton algorithm were involved. The feasibility was further tested using simulated data and real data, and the superiority was verified by comparison with the results obtained by the available method. Finally, we improved a method that can solve the singularity problem caused by the parameterization of the rotation matrix, which can be widely used in the robot-world and hand-eye calibration. The results show that the prediction errors of rotation and translation based on the proposed method be reduced to $0.26^\circ$ and $1.67$ mm, respectively.