Skip to main content
SpringerLink
Log in

Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative three-dimensional (3D) reconstructions that form the basis for guiding spine surgical procedures. Despite promising studies on this technology, there are few studies that offer insight into the robustness and generality of the approach by verifying performance in various testing scenarios. Therefore, this study aims at providing an assessment of a rUS system, with technical details from experiments starting at the bench-top to the pre-clinical study.

Methods

A semi-automatic control strategy was proposed to ensure continuous and smooth robotic scanning. Next, a U-Net-based segmentation approach was developed to automatically process the anatomic features and derive a high-quality 3D US reconstruction. Experiments were conducted on synthetic phantoms and human cadavers to validate the proposed approach.

Results

Average deviations of scanning force were found to be 2.84±0.45 N on synthetic phantoms and to be 5.64±1.10 N on human cadavers. The anatomic features could be reliably reconstructed at mean accuracy of 1.28±0.87 mm for the synthetic phantoms and of 1.74±0.89 mm for the human cadavers.

Conclusion

The results and experiments demonstrate the feasibility of the proposed system in a pre-clinical setting. This work is complementary to previous work, encouraging further exploration of the potential of this technology in in vivo studies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 10

Similar content being viewed by others

References

  1. Takahashi M, Iwamoto K, Kuzuyama M, Inami H, Matsumoto Y, Ueda S, Miyauchi Y (2021) Incidence of spinal instability among patients with discogenic low back pain with different backgrounds. J Phys Ther Sci 33(8):601–605. https://doi.org/10.1589/jpts.33.601

    Article  PubMed  PubMed Central  Google Scholar 

  2. Puvanesarajah V, Liauw JA, Lo S-F, Lina IA, Witham TF (2014) Techniques and accuracy of thoracolumbar pedicle screw placement. World J Orthop 5(2):112. https://doi.org/10.5312/wjo.v5.i2.112

    Article  PubMed  PubMed Central  Google Scholar 

  3. Gonzalez EA, Jain A, Bell MAL (2021) Combined ultrasound and photoacoustic image guidance of spinal pedicle cannulation demonstrated with intact ex vivo specimens. IEEE Trans Biomed Eng 68(8):2479–2489. https://doi.org/10.1109/TBME.2020.3046370

    Article  PubMed  PubMed Central  Google Scholar 

  4. Yan CX, Goulet B, Tampieri D, Collins DL (2012) Ultrasound-CT registration of vertebrae without reconstruction. Int J Comput Assist Radiol Surg 7(6):901–909. https://doi.org/10.1007/s11548-012-0771-9

    Article  PubMed  Google Scholar 

  5. Chen H-B, Zheng R, Qian L-Y, Liu F-Y, Song S, Zeng H-Y (2021) Improvement of 3-d ultrasound spine imaging technique using fast reconstruction algorithm. IEEE Trans Ultrason Ferroelectr Freq Control 68(10):3104–3113. https://doi.org/10.1109/TUFFC.2021.3087712

    Article  CAS  PubMed  Google Scholar 

  6. Ottacher D, Chan A, Parent E, Lou E (2020) Positional and orientational accuracy of 3-d ultrasound navigation system on vertebral phantom study. IEEE Trans Instrum Meas 69(9):6412–6419. https://doi.org/10.1109/TIM.2020.2973839

    Article  Google Scholar 

  7. Chen H, Zheng R, Lou E, Le LH (2020) Compact and wireless freehand 3d ultrasound real-time spine imaging system: a pilot study. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp. 2105–2108. https://doi.org/10.1109/EMBC44109.2020.9176614

  8. Jiang Z, Li Z, Grimm M, Zhou M, Esposito M, Wein W, Stechele W, Wendler T, Navab N (2022) Autonomous robotic screening of tubular structures based only on real-time ultrasound imaging feedback. IEEE Trans Industr Electron 69(7):7064–7075. https://doi.org/10.1109/TIE.2021.3095787

    Article  Google Scholar 

  9. Yang C, Jiang M, Chen M, Fu M, Li J, Huang Q (2021) Automatic 3d imaging and measurement of human spines with a robotic ultrasound system. IEEE Trans Instrum Meas 70:1–13. https://doi.org/10.1109/TIM.2021.3085110

    Article  Google Scholar 

  10. von Haxthausen F, Böttger S, Wulff D, Hagenah J, García-Vázquez V, Ipsen S (2021) Medical robotics for ultrasound imaging: current systems and future trends. Curr Robot Rep 2(1):55–71. https://doi.org/10.1007/s43154-020-00037-y

    Article  Google Scholar 

  11. Victorova M, Navarro-Alarcon D, Zheng Y-P (2019) 3d ultrasound imaging of scoliosis with force-sensitive robotic scanning. In: 2019 third IEEE international conference on robotic computing (IRC), pp. 262–265. https://doi.org/10.1109/IRC.2019.00049

  12. Zhang J, Wang Y, Liu T, Yang K, Jin H (2021) A flexible ultrasound scanning system for minimally invasive spinal surgery navigation. IEEE Trans Med Robot Bionics 3(2):426–435. https://doi.org/10.1109/TMRB.2021.3075750

    Article  Google Scholar 

  13. Xu K, Jiang B, Moghekar A, Kazanzides P, Boctor E (2022) Autoinfocus, a new paradigm for ultrasound-guided spine intervention: a multi-platform validation study. Int J Comput Assist Radiol Surg 17(5):911–920. https://doi.org/10.1007/s11548-022-02583-6

    Article  PubMed  Google Scholar 

  14. Zhang J, Liu T, Wang Y, Jiang W, Yang K, Jin H, Zhu Y (2022) Self-adaptive ultrasound scanning system for imaging human spine. IEEE Trans Industr Electron 69(1):570–581. https://doi.org/10.1109/TIE.2020.3047048

    Article  Google Scholar 

  15. Li R, Cai Y, Niu K, Poorten EV (2021) Comparative quantitative analysis of robotic ultrasound image calibration methods. In: 2021 20th international conference on advanced robotics (ICAR), pp. 511–516. https://doi.org/10.1109/ICAR53236.2021.9659341

  16. Ning G, Chen J, Zhang X, Liao H (2021) Force-guided autonomous robotic ultrasound scanning control method for soft uncertain environment. Int J Comput Assist Radiol Surg 16(12):2189–2199. https://doi.org/10.1007/s11548-021-02462-6

    Article  PubMed  Google Scholar 

  17. Aertbeliën E, De Schutter J (2014) eTaSL/eTC: a constraint-based task specification language and robot controller using expression graphs. In: 2014 IEEE/RSJ international conference on intelligent robots and systems, pp. 1540–1546. https://doi.org/10.1109/IROS.2014.6942760

  18. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sci 9351:234–241. https://doi.org/10.1007/978-3-319-24574-4_28

    Article  Google Scholar 

  19. Maier-Hein L, Menze B (2022) metrics reloaded: Pitfalls and recommendations for image analysis validation. arXiv: 2206.01653

  20. Li R, Niu K, Poorten EV (2021) A framework for fast automatic robot ultrasound calibration, pp. 1–7. https://doi.org/10.1109/ISMR48346.2021.9661495

  21. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. https://doi.org/10.1109/34.121791

    Article  Google Scholar 

  22. Tirindelli M, Victorova M, Esteban J, Kim ST, Navarro-Alarcon D, Zheng YP, Navab N (2020) Force-ultrasound fusion: bringing spine robotic-us to the next “level’’. IEEE Robot Autom Lett 5(4):5661–5668. https://doi.org/10.1109/LRA.2020.3009069

    Article  Google Scholar 

  23. Göbl R, Hennersperger C, Navab N (2022) Speckle2speckle: Unsupervised learning of ultrasound speckle filtering without clean data. arXiv: 2208.00402

  24. Alsinan AZ, Patel VM, Hacihaliloglu I (2019) automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int J Comput Assist Radiol Surg 14:775–783. https://doi.org/10.1007/s11548-019-01934-0

    Article  PubMed  Google Scholar 

  25. Garcia-Cano E, Cosio FA, Torres Robles F, Fanti Z, Bellefleur C, Joncas J, Labelle H, Duong L (2020) A freehand ultrasound framework for spine assessment in 3d: a preliminary study. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp. 2096–2100. https://doi.org/10.1109/EMBC44109.2020.9176689

  26. Ioannou C, Sarris I, Yaqub M, Noble J, Javaid M, Papageorghiou A (2011) surface area measurement using rendered three-dimensional ultrasound imaging: an in-vitro phantom study. Ultrasound Obstet Gynecol 38(4):445–449. https://doi.org/10.1002/uog.8984

  27. Chan A, Parent E, Mahood J, Lou E (2022) 3d ultrasound navigation system for screw insertion in posterior spine surgery: a phantom study. Int J Comput Assist Radiol Surg 17(2):271–281. https://doi.org/10.1007/s11548-021-02516-9

    Article  PubMed  Google Scholar 

  28. Daniel S, Aidana M, Christoph L, Fabio C, Mazda F, Fürnstahl P (2022) A posterior landmark-based registration-free method to identify pedicle screw trajectories for robot-based navigation: a proof-of-concept. In: 11th conference on new technologies for computer and robot assisted surgery

Download references

Funding

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 101016985 (FAROS) and Flemish Research Foundation (FWO) under grant agreement no. G0A1420N (Radar-spine) and no. 1S36322N (Harmony).

Author information

Authors and Affiliations

  1. Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium

    Ruixuan Li, Ayoob Davoodi, Yuyu Cai, Gianni Borghesan & Emmanuel Vander Poorten

  2. Robotics and Mechatronics, University of Twente, Enschede, The Netherlands

    Kenan Niu

  3. Core Lab ROB, Flanders Make, Leuven, Belgium

    Gianni Borghesan

  4. Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland

    Nicola Cavalcanti, Aidana Massalimova, Fabio Carrillo & Philipp Fürnstahl

  5. University Spine Center Zurich, University Hospital Balgrist, University of Zurich, Zurich, Switzerland

    Christoph J. Laux & Mazda Farshad

Authors
  1. Ruixuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayoob Davoodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuyu Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenan Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gianni Borghesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicola Cavalcanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aidana Massalimova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fabio Carrillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christoph J. Laux
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mazda Farshad
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Philipp Fürnstahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Emmanuel Vander Poorten
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruixuan Li.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Ethics approval

Ethical approval BASEC no. 202101196 was granted by the Cantonal Ethical Committee of the Canton of Zurich, Switzerland.

Informed consent

This study does not involve human participants.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, R., Davoodi, A., Cai, Y. et al. Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study. Int J CARS 18, 1613–1623 (2023). https://doi.org/10.1007/s11548-023-02932-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-023-02932-z

Keywords

Search

Navigation