[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Original Article
  • Published:
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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Contact authors.

Code availability

Custom code, contact authors.

References

  1. Baumhauer M, Feuerstein M, Meinzer HP, Rassweiler J (2008) Navigation in endoscopic soft tissue surgery: perspectives and limitations. J Endourol. https://doi.org/10.1089/end.2007.9827

    Article  PubMed  Google Scholar 

  2. Bernhardt S, Nicolau SA, Soler L, Doignon C (2017) The status of augmented reality in laparoscopic surgery as of 2016. Med Image Anal. https://doi.org/10.1016/j.media.2017.01.007

    Article  PubMed  Google Scholar 

  3. Altamar HO, Ong RE, Glisson CL, Viprakasit DP, Miga MI, Herrell SD, Galloway RL (2011) Kidney deformation and intraprocedural registration: a study of elements of image-guided kidney surgery. J Endourol. https://doi.org/10.1089/end.2010.0249

    Article  PubMed  Google Scholar 

  4. Maintz JB, Viergever MA (1998) A survey of medical image registration. Med Image Anal. https://doi.org/10.1016/s1361-8415(01)80026-8

    Article  PubMed  Google Scholar 

  5. Mezger U, Jendrewski C, Bartels M (2013) Navigation in surgery. Langenbecks Arch Surg. https://doi.org/10.1007/s00423-013-1059-4

    Article  PubMed  PubMed Central  Google Scholar 

  6. Giannarou S, Visentini-Scarzanella M, Yang GZ (2013) Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2012.81

    Article  PubMed  Google Scholar 

  7. Heiselman JS, Jarnagin WR, Miga MI (2020) Intraoperative correction of liver deformation using sparse surface and vascular features via linearized iterative boundary reconstruction. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.2967322

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rucker DC, Wu Y, Clements LW, Ondrake JE, Pheiffer TS, Simpson AL, Jarnagin WR, Miga MI (2014) A mechanics-based nonrigid registration method for liver surgery using sparse intraoperative data. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2013.2283016

    Article  PubMed  Google Scholar 

  9. Li C, Fan X, Hong J, Roberts DW, Aronson JP, Paulsen KD (2020) Model-based image updating for brain shift in deep brain stimulation electrode placement surgery. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2020.2990669

    Article  PubMed  PubMed Central  Google Scholar 

  10. García E, Diez Y, Diaz O, Lladó X, Martí R, Martí J, Oliver A (2018) A step-by-step review on patient-specific biomechanical finite element models for breast MRI to x-ray mammography registration. Med Phys. https://doi.org/10.1002/mp.12673

    Article  PubMed  Google Scholar 

  11. Benincasa AB, Clements LW, Herrell SD, Galloway RL (2008) Feasibility study for image-guided kidney surgery: assessment of required intraoperative surface for accurate physical to image space registrations. Med Phys doi 10(1118/1):2969064

    Google Scholar 

  12. Hughes-Hallett A, Mayer EK, Marcus HJ, Cundy TP, Pratt PJ, Darzi AW, Vale JA (2014) Augmented reality partial nephrectomy: examining the current status and future perspectives. Urology. https://doi.org/10.1016/j.urology.2013.08.049

    Article  PubMed  Google Scholar 

  13. Okamoto T, Onda S, Yanaga K, Suzuki N, Hattori A (2015) Clinical application of navigation surgery using augmented reality in the abdominal field. Surg Today. https://doi.org/10.1007/s00595-014-0946-9

    Article  PubMed  Google Scholar 

  14. Kikinis R, Pieper SD, Vosburgh KG (2014) 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz FA (ed) Intraoperative imaging and image-guided therapy. Springer, New York, pp 277–289

    Chapter  Google Scholar 

  15. Kokko MA, Seigne JD, Van Citters DW, Halter RJ (2020) Modeling the surgical exposure of anatomy in robot-assisted laparoscopic partial nephrectomy. Proc SPIE Med Imag. https://doi.org/10.1117/12.2550605

    Article  Google Scholar 

  16. Kokko MA, Seigne JD, Van Citters DW, Halter RJ (2021) Multi-body statistical shape representation of anatomy for navigation in robot-assisted laparoscopic partial nephrectomy. Proc SPIE Med Imag. https://doi.org/10.1117/12.2582320

    Article  Google Scholar 

  17. Sastry S (1999) Nonlinear systems: analysis, stability, and control. Springer, New York

    Book  Google Scholar 

  18. Moakher M (2002) Means and averaging in the group of rotations. SIAM J Matrix Anal Appl. https://doi.org/10.1137/S0895479801383877

    Article  Google Scholar 

  19. Muller ME (1959) A note on a method for generating points uniformly on n-dimensional spheres. Commun ACM. https://doi.org/10.1145/377939.377946

    Article  Google Scholar 

  20. Schneider C, Nguan C, Longpre M, Rohling R, Salcudean S (2013) Motion of the kidney between preoperative and intraoperative positioning. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2013.2239644

    Article  PubMed  Google Scholar 

  21. Uzosike AC, Patel HD, Alam R, Schwen ZR, Gupta M, Gorin MA, Johnson MH, Gausepohl H, Riffon MF, Trock BJ, Chang P, Wagner AA, Mckiernan JM, Allaf ME, Pierorazio PM (2018) Growth kinetics of small renal masses on active surveillance: variability and results from the DISSRM registry. J Urol. https://doi.org/10.1016/j.juro.2017.09.087

    Article  PubMed  Google Scholar 

  22. Simon D (2006) Optimal state estimation: Kalman, H [infinity] and nonlinear approaches. Wiley, Hoboken

    Book  Google Scholar 

  23. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge

    Google Scholar 

  24. Van Leeuwen PJ (2009) Particle filtering in geophysical systems. Mon Weather Rev. https://doi.org/10.1175/2009MWR2835.1

    Article  Google Scholar 

  25. Smith AFM, Gelfand AE (1992) Bayesian statistics without tears—sampling resampling perspective. Am Stat. https://doi.org/10.2307/2684170

    Article  Google Scholar 

  26. Myronenko A, Song X (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2010.46

    Article  PubMed  Google Scholar 

  27. Larcher A, Muttin F, Peyronnet B, De Naeyer G, Khene ZE, Dell’oglio P, Ferreiro C, Schatteman P, Capitanio U, D’hondt F, Montorsi F, Bensalah K, Mottrie A (2019) The learning curve for robot-assisted partial nephrectomy: impact of surgical experience on perioperative outcomes. Eur Urol. https://doi.org/10.1016/j.eururo.2018.08.042

    Article  PubMed  Google Scholar 

  28. Guend H, Widmar M, Patel S, Nash GM, Paty PB, Guillem JG, Temple LK, Garcia-Aguilar J, Weiser MR (2017) Developing a robotic colorectal cancer surgery program: understanding institutional and individual learning curves. Surg Endosc. https://doi.org/10.1007/s00464-016-5292-0

    Article  PubMed  Google Scholar 

  29. Mehaffey JH, Michaels AD, Mullen MG, Yount KW, Meneveau MO, Smith PW, Friel CM, Schirmer BD (2017) Adoption of robotics in a general surgery residency program: at what cost? J Surg Res. https://doi.org/10.1016/j.jss.2017.02.052

    Article  PubMed  PubMed Central  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Douglas W. Van Citters
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John D. Seigne
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryan J. Halter
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

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.

Consent to participate

Subjects signed informed consent.

Consent to publish

Subjects signed informed consent regarding publishing anonymized data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation