The development and error analysis of a kinematic parameters based spatial positioning method for an orthopedic navigation robot system

Influence of multi-angle input of intraoperative fluoroscopic images on the spatial positioning accuracy of the C-arm calibration-based algorithm of a CAOS system

Intraoperative fluoroscopic images, as one of the most important input data for computer-assisted orthopedic surgery (CAOS) systems, have a significant influence on the positioning accuracy of CAOS system. In this study, we proposed to use multi-angle intraoperative fluoroscopy images as input based on real clinical scenario, and the aim was to analyze the positioning accuracy and the error propagation rules with multi-angle input images compared with traditional two input images. In the experiment, the positioning accuracy of the C-arm calibration-based algorithm was studied, respectively, using two, three, four, five, and six intraoperative fluoroscopic images as input data. Moreover, the error propagation rules of the positioning error were analyzed by the Monte Carlo method. The experiment result showed that increasing the number of multi-angle input fluoroscopic images could reduce the positioning error of CAOS system, which has dropped from 1.01 to 0.61 mm. The Monte Carlo simulation analysis showed that for random input errors subject to normal distribution (μ = 0, σ = 1), the image positioning error dropped from 0.29 to 0.23 mm, and the staff gauge positioning error dropped from 1.36 to 1.19 mm, while the tracking device positioning error dropped from 3.41 to 2.13 mm. In addition, the results showed that image positioning error and staff gauge positioning error were all nonlinear error for the whole system, but tracker device positioning error was a strictly linear error. In conclusion, using multi-angle fluoroscopy images was helpful for clinic, which could improve the positioning accuracy of the CAOS system by nearly 30%. Graphical abstract The experiment process and Monte Carlo analysis of spatial positioning accuracy (A: Setup for the experiment; B: The process of Monte Carlo analysis; C: Results).

Influence of the quality of intraoperative fluoroscopic images on the spatial positioning accuracy of a CAOS system

BACKGROUND

Spatial positioning accuracy is a key issue in a computer-assisted orthopaedic surgery (CAOS) system. Since intraoperative fluoroscopic images are one of the most important input data to the CAOS system, the quality of these images should have a significant influence on the accuracy of the CAOS system. But the regularities and mechanism of the influence of the quality of intraoperative images on the accuracy of a CAOS system have yet to be studied.

METHODS

Two typical spatial positioning methods - a C-arm calibration-based method and a bi-planar positioning method - are used to study the influence of different image quality parameters, such as resolution, distortion, contrast and signal-to-noise ratio, on positioning accuracy. The error propagation rules of image error in different spatial positioning methods are analyzed by the Monte Carlo method.

RESULTS

Correlation analysis showed that resolution and distortion had a significant influence on spatial positioning accuracy. In addition the C-arm calibration-based method was more sensitive to image distortion, while the bi-planar positioning method was more susceptible to image resolution. The image contrast and signal-to-noise ratio have no significant influence on the spatial positioning accuracy. The result of Monte Carlo analysis proved that generally the bi-planar positioning method was more sensitive to image quality than the C-arm calibration-based method.

CONCLUSIONS

The quality of intraoperative fluoroscopic images is a key issue in the spatial positioning accuracy of a CAOS system. Although the 2 typical positioning methods have very similar mathematical principles, they showed different sensitivities to different image quality parameters. The result of this research may help to create a realistic standard for intraoperative fluoroscopic images for CAOS systems.