Skip to main content
SpringerLink
Log in

Comparison of Smartphone Augmented Reality, Smartglasses Augmented Reality, and 3D CBCT-guided Fluoroscopy Navigation for Percutaneous Needle Insertion: A Phantom Study

  • Laboratory Investigation
  • Other
  • Published:
CardioVascular and Interventional Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom.

Materials and Methods

An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cm2) and fluoroscopy time (sec) were recorded. Statistical comparisons were made using a two-way repeated measures ANOVA.

Results

The placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cm2, and no intra-procedural radiation was required for augmented reality.

Conclusions

Smartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

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

Similar content being viewed by others

References

  1. Racadio JM, Babic D, Homan R, Rampton JW, Patel MN, Racadio JM, et al. Live 3D guidance in the interventional radiology suite. AJR Am J Roentgenol. 2007;189(6):W357–64.

    Article  Google Scholar 

  2. Busser WM, Braak SJ, Futterer JJ, van Strijen MJ, Hoogeveen YL, de Lange F, et al. Cone beam CT guidance provides superior accuracy for complex needle paths compared with CT guidance. Br J Radiol. 2013;86(1030):20130310.

    Article  CAS  Google Scholar 

  3. Floridi C, Reginelli A, Capasso R, Fumarola E, Pesapane F, Barile A, et al. Percutaneous needle biopsy of mediastinal masses under C-arm conebeam CT guidance: diagnostic performance and safety. Med Oncol. 2017;34(4):67.

    Article  Google Scholar 

  4. Fior D, Vacirca F, Leni D, Pagni F, Ippolito D, Riva L, et al. Virtual guidance of percutaneous transthoracic needle biopsy with C-arm cone-beam CT: diagnostic accuracy, risk factors and effective radiation dose. Cardiovasc Intervent Radiol. 2019;42(5):712–9.

    Article  Google Scholar 

  5. Braak SJ, van Strijen MJ, van Leersum M, van Es HW, van Heesewijk JP. Real-Time 3D fluoroscopy guidance during needle interventions: technique, accuracy, and feasibility. AJR Am J Roentgenol. 2010;194(5):W445–51.

    Article  CAS  Google Scholar 

  6. Ahn SY, Park CM, Yoon SH, Kim H, Goo JM. Learning curve of C-arm cone-beam computed tomography virtual navigation-guided percutaneous transthoracic needle biopsy. Korean J Radiol. 2019;20(5):844–53.

    Article  Google Scholar 

  7. Wood BJ, Locklin JK, Viswanathan A, Kruecker J, Haemmerich D, Cebral J, et al. Technologies for guidance of radiofrequency ablation in the multimodality interventional suite of the future. J Vasc Interv Radiol. 2007;18(1 Pt 1):9–24.

    Article  Google Scholar 

  8. Park BJ, Hunt SJ, Martin C, Nadolski GJ, Wood BJ, Gade TP. Augmented and mixed reality: technologies for enhancing the future of IR. J Vasc Interv Radiol. 2020;31:1074–82.

    Article  Google Scholar 

  9. Uppot RN, Laguna B, McCarthy CJ, De Novi G, Phelps A, Siegel E, et al. Implementing virtual and augmented reality tools for radiology education and training, communication, and clinical care. Radiology. 2019;291(3):570–80.

    Article  Google Scholar 

  10. Kenngott HG, Preukschas AA, Wagner M, Nickel F, Muller M, Bellemann N, et al. Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study. Surg Endosc. 2018;32(6):2958–67.

    Article  Google Scholar 

  11. Heinrich F, Joeres F, Lawonn K, Hansen C. Comparison of projective augmented reality concepts to support medical needle insertion. IEEE Trans Vis Comput Graph. 2019;25(6):2157–67.

    Article  Google Scholar 

  12. Solbiati M, Passera KM, Rotilio A, Oliva F, Marre I, Goldberg SN, et al. Augmented reality for interventional oncology: proof-of-concept study of a novel high-end guidance system platform. Eur Radiol Exp. 2018;2:18.

    Article  Google Scholar 

  13. Elsayed M, Kadom N, Ghobadi C, Strauss B, Al Dandan O, Aggarwal A, et al. Virtual and augmented reality: potential applications in radiology. Acta Radiol. 2020. https://doi.org/10.1177/0284185119897362.

    Article  PubMed  Google Scholar 

  14. Pratt P, Ives M, Lawton G, Simmons J, Radev N, Spyropoulou L, et al. Through the HoloLens looking glass: augmented reality for extremity reconstruction surgery using 3D vascular models with perforating vessels. Eur Radiol Exp. 2018;2(1):2.

    Article  Google Scholar 

  15. Barsom EZ, Graafland M, Schijven MP. Systematic review on the effectiveness of augmented reality applications in medical training. Surg Endosc. 2016;30(10):4174–83.

    Article  CAS  Google Scholar 

  16. Li M, Seifabadi R, Long D, De Ruiter Q, Varble N, Hecht R, et al. Smartphone- versus smartglasses-based augmented reality (AR) for percutaneous needle interventions: system accuracy and feasibility study. Int J Computer Assist Radiol Surg. 2020;15:1921–30.

    Article  Google Scholar 

  17. Hecht R, Li M, de Ruiter QMB, Pritchard WF, Li X, Krishnasamy V, et al. Smartphone augmented reality CT-based platform for needle insertion guidance: a phantom study. Cardiovasc Intervent Radiol. 2020;43:756–64.

    Article  Google Scholar 

  18. Vovk A, Wild F, Guest W, Kuula T, editors. Simulator Sickness in Augmented Reality Training Using the Microsoft HoloLens. Conference on Human Factors in Computing Systems; 2018; Montreal, Canada. New York, NY United States: Association for Computing Machinery, New York, NY; 2018.

  19. Rebenitsch L, Owen C. Review on cybersickness in applications and visual displays. Virtual Real. 2016;20(2):101–35.

    Article  Google Scholar 

  20. Racadio JM, et al. Augmented reality on a C-arm system: a preclinical assessment for percutaneous needle localization. Radiology. 2016;281:249–55.

    Article  Google Scholar 

  21. Nicolau SA, Pennec X, Soler L, Buy X, Gangi A, Ayache N, et al. An augmented reality system for liver thermal ablation: design and evaluation on clinical cases. Med Image Anal. 2009;13(3):494–506.

    Article  CAS  Google Scholar 

  22. Fichtinger G, Deguet A, Masamune K, Balogh E, Fischer GS, Mathieu H, et al. Image overlay guidance for needle insertion in CT scanner. IEEE Trans Biomed Eng. 2005;52(8):1415–24.

    Article  Google Scholar 

  23. Si W, Liao X, Qian Y, Wang Q. Mixed reality guided radiofrequency needle placement: a pilot study. IEEE Access. 2018;6:31493–502.

    Article  Google Scholar 

  24. Lin MA, Siu AF, Bae JH, Cutkosky MR, Daniel BL. HoloNeedle: augmented reality guidance system for needle placement investigating the advantages of three-dimensional needle shape reconstruction. IEEE Robot Autom Lett. 2018;3(4):4156–62.

    Article  Google Scholar 

Download references

Funding

This work was supported by the Center for Interventional Oncology in the Intramural Research Program of the National Institutes of Health (NIH) by intramural NIH Grants NIH Z01 1ZID BC011242 and CL040015.

Author information

Authors and Affiliations

  1. Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA

    Dilara J. Long, Ming Li, Quirina M. B. De Ruiter, Rachel Hecht, Nicole Varble, Maxime Blain, Michael T. Kassin, Venkatesh P. Krishnasamy, William F. Pritchard, John W. Karanian, Bradford J. Wood & Sheng Xu

  2. Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA

    Xiaobai Li

  3. Philips Research of North America, Cambridge, MA, 02141, USA

    Nicole Varble

  4. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, DC, USA

    Karun V. Sharma

  5. Department of Interventional Radiology, George Washington University Hospital, Washington, DC, USA

    Shawn Sarin

Authors
  1. Dilara J. Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quirina M. B. De Ruiter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rachel Hecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaobai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicole Varble
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maxime Blain
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael T. Kassin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Karun V. Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shawn Sarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Venkatesh P. Krishnasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. William F. Pritchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. John W. Karanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Bradford J. Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Sheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Li.

Ethics declarations

Conflict of interest

NIH has a Cooperative Research and Development Agreements with Philips Research, Celsion Corp, Biocompatibles UK Ltd–Boston Scientific Corporation, Siemens Medical Solutions, NVIDIA, and XAct Robotics. None of these entities were involved in the reported work. NV is an employee of Philips Research North America. The content of this manuscript does not necessarily reflect the views, policies, or opinions of the U.S. Department of Health and Human Services. The mention of commercial products, their source, or their use in connection with material reported herein is not to be construed as an actual or implied endorsement of such products by the United States Government. Opinions expressed are those of the authors, not necessarily the NIH.

Ethical Approval

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

Informed Consent

For this type of study, informed consent is not required.

Consent for Publication

For this type of study, consent for publication is not required.

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

Long, D.J., Li, M., De Ruiter, Q.M.B. et al. Comparison of Smartphone Augmented Reality, Smartglasses Augmented Reality, and 3D CBCT-guided Fluoroscopy Navigation for Percutaneous Needle Insertion: A Phantom Study. Cardiovasc Intervent Radiol 44, 774–781 (2021). https://doi.org/10.1007/s00270-020-02760-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00270-020-02760-7

Keywords

Search

Navigation