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Optimization Model for the Distribution of Fiducial Markers in Liver Intervention

  • Systems-Level Quality Improvement
  • Published:
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Abstract

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

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Acknowledgements

The authors acknowledge the China Postdoctoral Science Foundation (Grant: 2018 T110880, and 2017 M620375), the National Natural Science Foundation of China (Grant: 81671788), the Guangdong Provincial Science and Technology Program (Grant: 2016A020220006, 2017B020210008, 2017B010110015, and 2017A040405054), the Fundamental Research Funds for the Central Universities (Grant: 2017ZD082), the Guangzhou Science and Technology Program (Grant: 201704020228).

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Author notes

  1. Qinyong Lin and Rongqian Yang contributed equally to this work.

Authors and Affiliations

  1. College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China

    Qinyong Lin & Ken Cai

  2. Department of Biomedical Engineering, South China University of Technology, Guangzhou, Guangdong, China

    Rongqian Yang

  3. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Lin Yang

  4. College of Science, Guilin University of Technology, Guilin, China

    Huazhou Chen

  5. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

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  1. Qinyong Lin
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  2. Rongqian Yang
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  3. Lin Yang
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  4. Huazhou Chen
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Correspondence to Ken Cai.

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Lin, Q., Yang, R., Yang, L. et al. Optimization Model for the Distribution of Fiducial Markers in Liver Intervention. J Med Syst 44, 83 (2020). https://doi.org/10.1007/s10916-020-01548-z

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