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Pre-clinical validation of virtual bronchoscopy using 3D Slicer

  • Pietro NardelliEmail author
  • Alexander Jaeger
  • Conor O’Shea
  • Kashif A. Khan
  • Marcus P. Kennedy
  • Pádraig Cantillon-Murphy
Original Article

Abstract

Purpose

Lung cancer still represents the leading cause of cancer-related death, and the long-term survival rate remains low. Computed tomography (CT) is currently the most common imaging modality for lung diseases recognition. The purpose of this work was to develop a simple and easily accessible virtual bronchoscopy system to be coupled with a customized electromagnetic (EM) tracking system for navigation in the lung and which requires as little user interaction as possible, while maintaining high usability.

Methods

The proposed method has been implemented as an extension to the open-source platform, 3D Slicer. It creates a virtual reconstruction of the airways starting from CT images for virtual navigation. It provides tools for pre-procedural planning and virtual navigation, and it has been optimized for use in combination with a \(5^{\circ }\) of freedom EM tracking sensor. Performance of the algorithm has been evaluated in ex vivo and in vivo testing.

Results

During ex vivo testing, nine volunteer physicians tested the implemented algorithm to navigate three separate targets placed inside a breathing pig lung model. In general, the system proved easy to use and accurate in replicating the clinical setting and seemed to help choose the correct path without any previous experience or image analysis. Two separate animal studies confirmed technical feasibility and usability of the system.

Conclusions

This work describes an easily accessible virtual bronchoscopy system for navigation in the lung. The system provides the user with a complete set of tools that facilitate navigation towards user-selected regions of interest. Results from ex vivo and in vivo studies showed that the system opens the way for potential future work with virtual navigation for safe and reliable airway disease diagnosis.

Keywords

Virtual bronchoscopy Computed tomography Airway segmentation 3D Slicer 

Notes

Acknowledgments

This study was funded by the “Irish Health Research Board” (POR /2012/31).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Animal rights

All applicable international, national and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the Irish Department of Health and UCC Animal Experimentation Ethics Committee.

Informed consent

This articles does not contain patient data.

References

  1. 1.
    Siegel R, Naishadham D, Jemal A (2012) Cancer statistics 2012. CA: Cancer J Clin 60(1):10–29Google Scholar
  2. 2.
    Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2013) GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC CancerBase, vol 11. International Agency for Research on Cancer, LyonGoogle Scholar
  3. 3.
    Eberhardt R, Anantham D, Herth F, Feller-Kopman D, Ernst A (2007) Electromagnetic navigation diagnostic bronchoscopy in peripheral lung lesions. CHEST J 131(6):1800–1805CrossRefGoogle Scholar
  4. 4.
    Chen A, Pastis N, Furukawa B, Silvestri GA (2015) The effect of respiratory motion on pulmonary nodule location during electromagnetic navigation bronchoscopy. CHEST J 147(5):1275–1281CrossRefGoogle Scholar
  5. 5.
    Eberhardt R, Kahn N, Gompelmann D, Schumann M, Heussel CP, Herth FJF (2010) LungPoint-a new approach to peripheral lesions. J Thoracic Oncol 5(10):1559–1563CrossRefGoogle Scholar
  6. 6.
    Solomon SB, White P, Wiener CM, Orens JB, Wang KP (2000) Three-dimensional CT-guided bronchoscopy with a real-time electromagnetic position sensor: a comparison of two image registration methods. CHEST J 118(6):1783–1787CrossRefGoogle Scholar
  7. 7.
    Becker HD, Herth F, Ernst A, Shwarz Y (2005) Bronchoscopic biopsy of peripheral lung lesions under electromagnetic guidance: a pilot study. J Bronchol Interv Pulmonol 12(1):9–13Google Scholar
  8. 8.
    Bricault I, Ferretti G, Cinquin P (1998) Registration of real and CTderived virtual bronchoscopic images to assist transbronchial biopsy. IEEE Trans Med Imag 17(5):703–714CrossRefGoogle Scholar
  9. 9.
    Chung AJ, Deligianni F, Shah P, Wells A, Yang GZ (2006) Patient specific bronchoscopy visualization through BRDF estimation and disocclusion correction. IEEE Trans Med Imag 25(4):503–513CrossRefGoogle Scholar
  10. 10.
    Deligianni F, Chung A, Yang G-Z (2003) pq-Space based 2D/3D registration for endoscope tracking. Med Image Comput Comput Assist Interv (MICCAI) 2878:311–318Google Scholar
  11. 11.
    Deligianni F, Chung A, Yang G-Z (2004) Patient-specific bronchoscope simulation with pq-space-based 2D/3D registration. Comput Aided Surg 9(5):215–226PubMedGoogle Scholar
  12. 12.
    Deligianni F, Chung A, Yang G-Z (2004) Patient-specific bronchoscope simulation with pq-space-based 2D/3D registration. Comput Aided Surg 9(5):215–226PubMedGoogle Scholar
  13. 13.
    Deligianni F, Chung A, Yang G-Z (2006) Nonrigid 2-D/3-D registration for patient specific bronchoscopy simulation with statistical shape modeling: Phantom validation. IEEE Trans Med Imag 25(11):1462–1471CrossRefGoogle Scholar
  14. 14.
    Shen M, Giannarou S, Yang G-Z (2015) Robust camera localisation with depth reconstruction for bronchoscopic navigation. Int J Comput Assist Radiol Surg CARS 10(6):801–813CrossRefGoogle Scholar
  15. 15.
    Mori K, Deguchi D, Akiyama K, Kitasaka T, Maurer CR Jr, Yasuhito Suenaga, Takabatake H, Mori M, Natori H (2005) Hybrid bronchoscope tracking using a magnetic tracking sensor and image registration. Med Image Comput Comput Assist Interv (MICCAI) 3750:543–550Google Scholar
  16. 16.
    Luo X, Feuerstein M, Sugiura T, Kitasaka T, Imaizumi K, Hasegawa Y, Mori K (2010) Towards hybrid bronchoscope tracking under respiratory motion: evaluation on a dynamic motion phantom. In: SPIE medical imaging. International Society for Optics and Photonic, p 76251BGoogle Scholar
  17. 17.
    Soper TD, Haynor DR, Glenny RW, Seibel EJ (2009) Validation of CT-video registration for guiding a novel ultrathin bronchoscope to peripheral lung nodules using electromagnetic tracking. In: SPIE medical imaging. International Society for Optics and Photonic, p 72610CGoogle Scholar
  18. 18.
    Soper TD, Haynor DR, Glenny RW, Seibel EJ (2010) In vivo validation of a hybrid tracking system for navigation of an ultrathin bronchoscope within peripheral airways. IEEE Trans Biomed Eng 57(3):736–745CrossRefPubMedGoogle Scholar
  19. 19.
    Higgins WE, Helferty JP, Lu K, Merritt SA, Rai L, Yu K-C (2008) 3D CT-video fusion for image-guided bronchoscopy. Comput Med Imag Graph 32(3):159–173CrossRefGoogle Scholar
  20. 20.
    Nagao J, Mori K, Enjouji T, Deguchi D, Kitasaka T, Suenaga Y, Hasegawa J, Toriwaki J, Takabatake H, Natori H (2004) Fast and accurate bronchoscope tracking using image registration and motion prediction. Med Image Comput Comput Assist Interv (MICCAI) 3217:551–558Google Scholar
  21. 21.
    Wegner I, Tetzlaff R, Biederer J, Wolf I, Meinzer HP (2008) An evaluation environment for respiratory motion compensation in navigated bronchoscopy. In: SPIE medical imaging. International Society for Optics and Photonics, p 691811Google Scholar
  22. 22.
    Gergel I, dos Santos TR, Tetzlaff R, Maier-Hein L, Meinzer HP, Wegner I (2010) Particle filtering for respiratory motion compensation during navigated bronchoscopy. In: SPIE medical imaging. International Society for Optics and Photonics, p 76250WGoogle Scholar
  23. 23.
    O’Donoghue K, Eustace D, Griffiths J, OShea M, Power T, Cantillon-Murphy P (2014) Catheter position tracking system using planar magnetics and closed loop current control. IEEE Trans Magn 50(7):1–9CrossRefGoogle Scholar
  24. 24.
    O’Donoghue K., Corvo A, Nardelli P, O’Shea C, Ali Khan K, Kennedy MP, Cantillon-Murphy P (2014) Evaluation of a novel tracking system in a breathing lung model. In: 36th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 4046–4049Google Scholar
  25. 25.
    O’Donoghue K, Cantillon-Murphy P (2015) Planar magnetic shielding for use with electromagnetic tracking systems. IEEE Trans Magn 51(2):1–12CrossRefGoogle Scholar
  26. 26.
    O’Donoghue K, Cantillon-Murphy P (2015) Low cost super-Nyquist asynchronous demodulation for use in EM tracking systems. IEEE Trans Instrum Meas 64(2):458–466CrossRefGoogle Scholar
  27. 27.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Fillion-Robin JFJC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imag 30(9):1323–1341CrossRefGoogle Scholar
  28. 28.
    Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 10:2527–2537CrossRefGoogle Scholar
  29. 29.
    Bouix S, Siddiqi K, Tannenbaum A (2005) Flux driven automatic centerline extraction. Med Image Anal 9(3):209–221CrossRefPubMedGoogle Scholar
  30. 30.
    Siddiqi K, Kimia BB, Shu C (1997) Geometric shock-capturing ENO schemes for sub-pixel interpolation, computation and curve evolution. CVGIP Graph Models Image Process 59(5):278–301CrossRefGoogle Scholar
  31. 31.
    Voronoi G (1908) Nouvelles applications des paramtres continus la thorie des formes quadratiques. Premier mmoire. Sur quelques proprits des formes quadratiques positives parfaites. J fr die reine und angewandte Mathematik 133:97–178Google Scholar
  32. 32.
    Nardelli P, Khan KA, Corvó A, Moore N, Murphy MJ, Twomey M, O’Connor OJ, Kennedy MP, Estépar RSJ, Maher MM, Cantillon-Murphy P (2015) Optimizing parameters of an open-source airway segmentation algorithm using different CT images. Biomed Eng Online 14(1):62CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Brooke J (1996) SUS—a quick and dirty usability scale. Usability Eval Ind 189(194):4–7Google Scholar
  34. 34.
    Bangor A, Kortum P, Miller J (2009) Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 4(3):114–123Google Scholar
  35. 35.
    O’Shea C, Khan KA, Nardelli P, Jaeger A, Kennedy MP, Cantillon-Murphy P (2015) Evaluation of endoscopically deployed radiopaque tumour models in bronchoscopy. J Bronchol Interv Pulmonol 23(2):112–122Google Scholar
  36. 36.
    Deguchi D, Feuerstein M, Kitasaka T, Suenaga Y, Ide I, Murase H, Imaizumi K, Hasegawa Y, Mori K (2012) Real-time marker-free patient registration for electromagnetic navigated bronchoscopy: a phantom study. Int J Comput Assist Radiol Surg (CARS) 7(3):359–369CrossRefGoogle Scholar
  37. 37.
    Hofstad EF, Sorger H, Leira HO, Amundsen T, Lango T (2014) Automatic registration of CT images to patient during the initial phase of bronchoscopy: a clinical pilot study. Med Phys 4(41):041903Google Scholar
  38. 38.
    Merritt S, Khare R, Bascom R, Higgins W (2013) Interactive CT-video registration for the continuous guidance of bronchoscopy. IEEE Trans Med Imag 32(8):1376–1396CrossRefGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  • Pietro Nardelli
    • 1
    Email author
  • Alexander Jaeger
    • 1
  • Conor O’Shea
    • 1
  • Kashif A. Khan
    • 2
  • Marcus P. Kennedy
    • 2
  • Pádraig Cantillon-Murphy
    • 1
  1. 1.School of EngineeringUniversity College CorkCorkIreland
  2. 2.Department of Respiratory MedicineCork University HospitalWilton, CorkIreland

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