Feasibility of respiratory motion-compensated stereoscopic X-ray tracking for bronchoscopy

  • Nikolas Leßmann
  • Daniel Drömann
  • Alexander Schlaefer
Original Article
  • 210 Downloads

Abstract

Purpose

   Precise localization in bronchoscopy is challenging, particularly for peripheral lesions that cannot be reached by conventional bronchoscopes with a large working channel. Existing navigation methods are hampered by respiratory motion, e.g., in the lower lobes. We present an image-guided approach that considers respiratory motion and can localize instruments.

Methods

   We developed a rigid chest marker containing steel balls visible in X-ray images and a pattern for passive tracking with an optical camera system. An experimental setup to evaluate stereoscopic localization and to mimic chest motion was established in our interventional suite. The marker motion was recorded, and X-ray images were acquired from different angles using a standard C-arm. All coordinates were expressed with respect to the stationary tracking camera. The feasibility of motion-compensated stereoscopic localization was assessed.

Results

   The orientation of the C-arm could be established with a mean error of less than \(1^{\circ }\). Triangulation based on two different X-ray images from different angles resulted in a mean error of 1.8 (\(\pm \)0.7) mm. A similar result was obtained when the marker was moved between X-ray acquisitions, and the mean error was 1.6 (\(\pm \)1.4) mm. The latencies were approximately 80 and 380 ms for tracking camera and X-ray imaging, respectively. Stereoscopic localization of a moving target was feasible.

Conclusions

   The system presents a flexible alternative for precise stereoscopic localization of a bronchoscope or instruments using a standard C-arm. We demonstrated the ability to track multiple moving markers and to compensate for respiratory motion.

Keywords

Bronchoscopy Navigation C-arm  Respiratory motion Stereoscopy 

Notes

Acknowledgments

We would like to thank Pedro Névoa and Julian Sulikowski for helping setup the experimental environment.

Conflict of interest

N. Leßmann, D. Drömann, and A. Schlaefer declare that they have no conflict of interest.

References

  1. 1.
    Rooney CP, Wolf K, McLennan G (2002) Ultrathin bronchoscopy as an adjunct to standard bronchoscopy in the diagnosis of peripheral lung lesions. Respiration 69(1):63–68PubMedCrossRefGoogle Scholar
  2. 2.
    Herth F, Eberhardt R, Ernst A (2006) The future of bronchoscopy in diagnosing, staging and treatment of lung cancer. Respiration 73:399–409PubMedGoogle Scholar
  3. 3.
    Harms W, Krempien R, Grehn C, Hensley F, Debus J, Becker HD (2006) Electromagnetically navigated brachytherapy as a new treatment option for peripheral pulmonary tumors. Strahlenther Onkol 182(2):108–111PubMedCrossRefGoogle Scholar
  4. 4.
    Tachihara M, Ishida T, Kanazawa K, Sugawara A, Watanabe K, Uekita K, Moriya H, Yamazaki K, Asano F, Munakata M (2007) A virtual bronchoscopic navigation system under X-ray fluoroscopy for transbronchial diagnosis of small peripheral pulmonary lesions. Lung Cancer 57(3):322–327PubMedCrossRefGoogle Scholar
  5. 5.
    Merritt SA, Gibbs JD, Yu K-C, Patel V, Rai L, Cornish DC, Bascom R, Higgins WE (2008) Image-guided bronchoscopy for peripheral lung lesions: a phantom study. Chest 134(5):1017–1026PubMedCrossRefGoogle Scholar
  6. 6.
    Schwarz Y, Mehta AC, Ernst A, Herth F, Engel A, Besser D, Becker HD (2003) Electromagnetic navigation during flexible bronchoscopy. Respiration 70(5):516–522PubMedCrossRefGoogle Scholar
  7. 7.
    Schwarz Y, Greif J, Becker HD, Ernst A, Mehta A (2006) Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid CT images: the first human study. Chest 129(4):988–994PubMedCrossRefGoogle Scholar
  8. 8.
    Eberhardt R, Anantham D, Herth F, Feller-Kopman D, Ernst A (2007) Electromagnetic navigation diagnostic bronchoscopy in peripheral lung lesions. Chest. 131(6):1800–1805PubMedCrossRefGoogle Scholar
  9. 9.
    Leong S, Ju H, Marshall H, Bowman R, Yang I, Ree AM, Saxon C, Fong KM (2012) Electromagnetic navigation bronchoscopy: a descriptive analysis. J Thorac Dis 4(2):173–185PubMedCentralPubMedGoogle Scholar
  10. 10.
    Deguchi D, Akiyama K, Mori K, Kitasaka T, Suenaga Y, Maurer CR Jr, Takabatake H, Mori M, Natori H (2006) A method for bronchoscope tracking by combining a position sensor and image registration. Comput Aided Surg 11(3):109–117PubMedGoogle Scholar
  11. 11.
    Ishida T, Asano F, Yamazaki K, Shinagawa N, Oizumi S, Moriya H, Munakata M, Nishimura M (2011) Virtual bronchoscopic navigation combined with endobronchial ultrasound to diagnose small peripheral pulmonary lesions: a randomised trial. Thorax 66(12):1072–1077PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Eberhardt R, Anantham D, Ernst A, Feller-Kopman D, Herth F (2007) Multimodality bronchoscopic diagnosis of peripheral lung lesions: a randomized controlled trial. Am J Respir Crit Care Med 176(1):36–41PubMedCrossRefGoogle Scholar
  13. 13.
    Gergel I, Hering J, Tetzlaff R, Meinzer HP, Wegner I (2011) An electromagnetic navigation system for transbronchial interventions with a novel approach to respiratory motion compensation. Med Phys 38(12):6742–6753PubMedCrossRefGoogle Scholar
  14. 14.
    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):736–745PubMedCrossRefGoogle Scholar
  15. 15.
    Brost A, Liao R, Strobel N, Hornegger J (2010) Respiratory motion compensation by model-based catheter tracking during EP procedures. Med Image Anal 14(5):695–706PubMedCrossRefGoogle Scholar
  16. 16.
    Binder N, Matthäus L, Burgkart R, Schweikard A (2005) A robotic C-arm fluoroscope. Int J Med Robot 1(3):108–116PubMedCrossRefGoogle Scholar
  17. 17.
    Bodensteiner C, Darolti C, Schumacher H, Matthäus L, Schweikard A (2007) Motion and positional error correction for cone beam 3D-reconstruction with mobile C-arms. Med Image Comput Comput Assist Interv 10(Pt 1):177–185PubMedGoogle Scholar
  18. 18.
    Wang L, Fallavollita P, Zou R, Chen X, Weidert S, Navab N (2012) Closed-form inverse kinematics for interventional C-arm X-ray imaging with six degrees of freedom: modeling and application. IEEE Trans Med Imaging 31(5):1086–1099PubMedCrossRefGoogle Scholar
  19. 19.
    Nozaki T, Fujiuchi Y, Komiya A, Fuse H (2012) Efficacy of DynaCT for surgical navigation during complex laparoscopic surgery: an initial experience. Surg Endosc 27(3):903–909Google Scholar
  20. 20.
    Navab N, Wiesner S, Benhimane S, Euler E, Heining SM (2006) Visual servoing for intraoperative positioning and repositioning of mobile C-arms. Med Image Comput Comput Assist Interv 9(Pt 1):551–560PubMedGoogle Scholar
  21. 21.
    Rougée A, Picard C, Ponchut C, Trousset Y (1993) Geometrical calibration of x-ray imaging chains for three-dimensional reconstruction. Comput Med Imaging Graph 17(4/5):295–300PubMedCrossRefGoogle Scholar
  22. 22.
    Fallavollita P, Burdette EC, Song DY, Abolmaesumi P, Fichtinger G (2011) Technical note: unsupervised C-arm pose tracking with radiographic fiducial. Med Phys 38(4):2241–2245PubMedCrossRefGoogle Scholar
  23. 23.
    Jain AK, Mustafa T, Zhou Y, Burdette C, Chirikjian GS, Fichtinger G (2005) FTRAC—a robust fluoroscope tracking fiducial. Med Phys 32:3185–3198PubMedCrossRefGoogle Scholar
  24. 24.
    Brost A, Strobel N, Yatziv L, Gilson W, Meyer B, Hornegger J, Lewin J, Wacker F (2009) Accuracy of X-ray image-based 3D localization from two c-arm views: a comparison between an ideal system and a real device. In: Miga MI, Wong KH (eds) Proceedings of SPIE medical imaging 2009: visualization, image-guided procedures, and modelingGoogle Scholar
  25. 25.
    Chen SY, Metz CE (1997) Improved determination of biplane imaging geometry from two projection images and its application to three-dimensional reconstruction of coronary arterial trees. Med Phys 24(5):633–654PubMedCrossRefGoogle Scholar
  26. 26.
    Murphy MJ (1997) An automatic six-degree-of-freedom image registration algorithm for image-guided frameless stereotaxic radiosurgery. Med Phys. 24:857–866PubMedCrossRefGoogle Scholar
  27. 27.
    Kilby W, Dooley JR, Kuduvalli G, Sayeh S, Maurer CR Jr (2010) The CyberKnife robotic radiosurgery system in 2010. Technol Cancer Res Treat 9(5):433–452PubMedGoogle Scholar
  28. 28.
    DeMenthon DF, Davis LS (1995) Model-based object pose in 25 lines of code. Int J Comput Vis 15:123–141CrossRefGoogle Scholar
  29. 29.
    Ritchie CJ, Hsieh J, Gard MF, Godwin JD, Kim Y, Crawford CR (1994) Predictive respiratory gating: a new method to reduce motion artifacts on CT scans. Radiology 190(3):847– 852Google Scholar
  30. 30.
    Kubo HD, Hill BC (1996) Respiration gated radiotherapy treatment: a technical study. Phys Med Biol 41(1):83–91PubMedCrossRefGoogle Scholar
  31. 31.
    Schweikard A, Glosser G, Bodduluri M, Murphy MJ, Adler JR (2000) Robotic motion compensation for respiratory movement during radiosurgery. Comput Aided Surg 5(4):263–277PubMedCrossRefGoogle Scholar
  32. 32.
    Murphy MJ (2004) Tracking moving organs in real time. Semin Radiat Oncol 14(1):91–100PubMedCrossRefGoogle Scholar
  33. 33.
    Ernst F, Schlaefer A, Schweikard A (2010) Smoothing of respiratory motion traces for motion-compensated radiotherapy. Med Phys 37(1):282–294PubMedCrossRefGoogle Scholar
  34. 34.
    Ernst F, Bruder R, Schlaefer A, Schweikard A (2012) Correlation between external and internal respiratory motion: a validation study. Int J Comput Assist Radiol Surg 7(3):483–492PubMedCrossRefGoogle Scholar
  35. 35.
    Yan H, Yin FF, Zhu GP, Ajlouni M, Kim JH (2006) The correlation evaluation of a tumor tracking system using multiple external markers. Med Phys 33(11):4073–4084PubMedCrossRefGoogle Scholar
  36. 36.
    Bradski G (2000) The OpenCV library. Dr. Dobb’s Journal of Software ToolsGoogle Scholar
  37. 37.
    Hartley R, Sturm P (1995) Triangulation. In: Hlaváč V, Šára R (eds) Computer analysis of images and patterns, volume 970 of Lecture Notes in computer science. Springer, Berlin, pp 190–197Google Scholar
  38. 38.
    Maier-Hein L, Franz AM, Birkfellner W, Hummel J, Gergel I, Wegner I, Meinzer HP (2012) Standardized assessment of new electromagnetic field generators in an interventional radiology setting. Med Phys 39(6):3424–3434PubMedCrossRefGoogle Scholar
  39. 39.
    Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV, Miyasaka K (2002) Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 53:822–834Google Scholar
  40. 40.
    Fu D, Kuduvalli G, Maurer CR Jr, Allison JW, Adler JR Jr (2006) 3D target localization using 2D local displacements of skeletal structures in orthogonal x-ray images for image-guided spinal radiosurgery. Int J Comput Assist Radiol Surg 1:198–200Google Scholar
  41. 41.
    Fu D, Kahn R, Wang B, Wang H, Mu Z, Park J, Kuduvalli G, Maurer CR Jr (2007) Xsight lung tracking system: a fiducial-less method for respiratory motion tracking. In: Urschel HC Jr, Kresl JJ, Luketich JD, Papiez L, Timmerman RD (eds) Robotic radiosurgery: treating tumors that move with respiration. Springer, Berlin, pp 265–282CrossRefGoogle Scholar

Copyright information

© CARS 2013

Authors and Affiliations

  • Nikolas Leßmann
    • 1
  • Daniel Drömann
    • 2
  • Alexander Schlaefer
    • 1
  1. 1.Medical Robotics Group, Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany
  2. 2.Medizinische Klinik IIIUniversitätsklinikum Schleswig-Holstein, Campus LübeckLübeckGermany

Personalised recommendations