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A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure

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

Abstract

Purpose

Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest.

Methods

Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence.

Results

In the static case, both particle filtering and persistent CPD achieved sub-5 mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence.

Conclusion

This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.

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Acknowledgements

The authors are grateful for assistance from Tracy Stokes and Tracy Frazee.

Funding

Norris Cotton Cancer Center pilot Grant.

Author information

Authors and Affiliations

  1. Thayer School of Engineering, Dartmouth College, 15 Thayer Drive, Hanover, NH, 03755, USA

    Michael A. Kokko, Douglas W. Van Citters & Ryan J. Halter

  2. Dartmouth-Hitchcock Medical Center, Section of Urology, 1 Medical Center Drive, Lebanon, NH, 03756, USA

    John D. Seigne

  3. Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Road, Hanover, NH, 03755, USA

    John D. Seigne & Ryan J. Halter

Authors
  1. Michael A. Kokko
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  2. Douglas W. Van Citters
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  3. John D. Seigne
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  4. Ryan J. Halter
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Contributions

MAK collected and analyzed data, and drafted the manuscript in collaboration with RJH and DWVC. JDS performed surgery, data collection, and provided clinical insight. All authors reviewed the manuscript.

Corresponding author

Correspondence to Michael A. Kokko.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Study approved by Dartmouth CPHS and Dartmouth-Hitchcock Health IRB; all ethical standards were followed.

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Subjects signed informed consent.

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Subjects signed informed consent regarding publishing anonymized data.

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Kokko, M.A., Van Citters, D.W., Seigne, J.D. et al. A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure. Int J CARS 17, 1079–1089 (2022). https://doi.org/10.1007/s11548-022-02638-8

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  • DOI: https://doi.org/10.1007/s11548-022-02638-8

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