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Markerless robotic pedicle screw placement based on structured light tracking

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Most existing robot systems for pedicle screw placement rely on optical markers to establish the spatial relationship between the surgical tool and the surgical path. Marker installation and registration are time-consuming, and error may also accumulate along the complicated coordinate transformation chain. Therefore, we proposed a markerless structured light-based method to simplify the surgery workflow and the coordinate transformation chain.

Methods

Firstly, a structured light camera is used to directly track both the surgical tool and the bone anatomy without using markers. Secondly, a markerless “two-direction” approach for robot-camera registration together with a feedback robot control method is developed. Lastly, a prototype system is built and examined with precision validation experiments and pedicle screw drilling experiments.

Results

Precision validation experiments show satisfactory positioning accuracy of the system. In drilling experiments, 42 paths were drilled on three synthesized cervical vertebrae phantoms and all the paths successfully went through the pedicles. The mean position error of the entry point was 0.28 ± 0.16 mm, and the mean angle error was 0.49 ± 0.24°, which can meet the clinical requirement.

Conclusion

The results show the feasibility of the proposed structured light-based method for pedicle screw placement, which has a simple workflow and can achieve good accuracy without using optical markers.

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Acknowledgements

This work is sponsored by Tsinghua University and Cyrus Tang Foundation.

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Authors and Affiliations

  1. School of Aerospace Engineering, Tsinghua University, Beijing, China

    Shijie Zhu & Gangtie Zheng

  2. Department of Orthopaedics, Beijing Tsinghua Changgung Hospital, Beijing, China

    Zhe Zhao & Yongwei Pan

  3. School of Clinical Medicine, Tsinghua University, Beijing, China

    Zhe Zhao & Yongwei Pan

Authors
  1. Shijie Zhu
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  2. Zhe Zhao
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  4. Gangtie Zheng
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Correspondence to Gangtie Zheng.

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Appendix: Technical details for point cloud registration algorithms

Appendix: Technical details for point cloud registration algorithms

Data preprocessing

3D models of the vertebrae are reconstructed from CT data using Mimics 20.0. Point clouds of vertebrae and the surgical tool are uniformly sampled from 3D models using 3-Matic 12.0. The mean distance between the adjacent points is 0.4 mm, which is consistent with the scanned point clouds. Before registration, the segmented scanned point clouds (Figs. 2b, 4b) are denoised (built-in function “pcdenoise” of Matlab2019a, with default parameters, see https://www.mathworks.com/help/vision/ref/pcdenoise.html for more details).

Parameter setting for Super4PCS algorithm

The Super4PCSLibrary [26] is used, with registration parameters given in Table 3. As the scanned point cloud of the tool is accurately segmented, a larger overlap ratio is assigned for computational efficiency (see http://nmellado.github.io/Super4PCS/a05043.html. for more details).

Table 3 Parameter setting for Super4PCS algorithm
Full size table

Parameter setting for ICP algorithm

The built-in ICP function “pcregistericp” of Matlab2019a is used, with parameters listed in Table 4. In this basic ICP implementation, a nearest-neighbor search, an outlier check, and a rigid transformation calculation are performed in every iteration (for more details, see https://www.mathworks.com/help/vision/ref/pcregistericp.html).

Table 4 Parameter setting for ICP algorithm
Full size table

The only thing we modified is the outlier checker. The built-in checker keeps the closest point pairs as inliers according to the given inlier ratio. This strategy works well for the tool, but not for the bone due to the uncertain overlap ratio resulting from manual annotations. Therefore, a hybrid outlier rejection strategy is used for bone registration, i.e., point pairs closer than the given inlier distance or within the inlier ratio are kept. On the one hand, the fixed distance threshold keeps as many valid points as possible when close to convergence. On the other hand, the inlier ratio criterion avoids the local minima caused by insufficient inliers obtained through the inlier distance criterion, especially in early iterations.

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Zhu, S., Zhao, Z., Pan, Y. et al. Markerless robotic pedicle screw placement based on structured light tracking. Int J CARS 15, 1347–1358 (2020). https://doi.org/10.1007/s11548-020-02215-x

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  • DOI: https://doi.org/10.1007/s11548-020-02215-x

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