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A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy

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

Abstract

Purpose

The purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the accuracy of 1 mm or even higher without bone-implanted markers.

Methods

A hybrid feature-based registration method termed CR-RAMSICP is proposed. Point-based coarse registration (CR) is accomplished relying on the optical retro-reflective markers attached to the tracked rigid body fixed out of the bone. In surface-based fine registration, an improved iterative closest point (ICP) algorithm based on the range-adaptive matching strategy (termed RAMSICP) is presented to cope with the robust precise matching between the asymmetric patient and image point clouds, which avoids converging to a local minimum.

Results

A series of registration experiments based on the isolated porcine iliums are carried out. The results illustrate that CR-RAMSICP not only significantly outperforms CR and CR-ICP in the accuracy and reproducibility, but also exhibits better robustness to the CR errors and less sensitiveness to the distribution and number of fiducial points located in the patient point cloud than CR-ICP.

Conclusion

The proposed registration method CR-RAMSICP can stably satisfy the desired registration accuracy without the use of bone-implanted markers like fiducial screws. Besides, the RAMSICP algorithm used in fine registration is convenient for programming because any complex metrics or models are not involved.

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Availability of data and material

All experimental data in this study are included in this article.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 51875094) and the Fundamental Research Funds for the Central Universities (Grant Nos. N2003011).

Funding

This study was supported by the National Natural Science Foundation of China (Grant Nos. 51875094) and the Fundamental Research Funds for the Central Universities (Grant Nos. N2003011).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

    Chunlei Zhang, Yu Liu & He Li

  2. Shenyang Xingwen Technology Co., Ltd, Shenyang, China

    Yunguang Zhang

Authors
  1. Chunlei Zhang
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  2. Yu Liu
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  3. Yunguang Zhang
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  4. He Li
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Contributions

CZ was involved in conceptualization; CZ and YL were involved in methodology; CZ, YL, and YZ were involved in formal analysis and investigation; CZ was involved in validation; CZ was involved in software and visualization; CZ was involved in data curation; CZ was involved in writing—original draft preparation; CZ, YL, and HL were involved in writing—review and editing; YL was involved in funding acquisition; YL and YZ were involved in resources; YL and YZ were involved in project administration; YL, YZ, and HL were involved in supervision.

Corresponding author

Correspondence to Yu Liu.

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Informed consent was obtained from all individual participants included in the study.

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This article does not contain any study with human participants or animals performed by any of the authors.

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Zhang, C., Liu, Y., Zhang, Y. et al. A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy. Int J CARS 16, 1507–1516 (2021). https://doi.org/10.1007/s11548-021-02439-5

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  • DOI: https://doi.org/10.1007/s11548-021-02439-5

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