3D Catheter Tip Tracking in 2D X-Ray Image Sequences Using a Hidden Markov Model and 3D Rotational Angiography

  • Pierre AmbrosiniEmail author
  • Ihor Smal
  • Daniel Ruijters
  • Wiro J. Niessen
  • Adriaan Moelker
  • Theo van Walsum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9365)


Integration of pre- or peri-operative images may improve image guidance in minimally invasive interventions. In abdominal catheterization procedures such as transcatheter arterial chemoembolization, 3D pre-/peri-operative images contain relevant information, such as complete 3D vasculature, that is not directly available from 2D imaging. Accurate knowledge of the catheter tip position in 3D is currently not available, and after registration of 3D information to 2D images (angiographies), the registration is invalidated by breathing motion and thus requires continuous updates. We propose a hidden Markov model based method to track the 3D catheter position, using 2D fluoroscopic image sequences and a 3D vessel tree obtained from 3D Rotational Angiography. Such a tracking facilitates display of the catheter in the 3D anatomy, and it enables to use the 3D vessels as a roadmap in 2D imaging. The tracking is initialized with the first 2D image of the sequence. For the subsequent images, based on a state transition probability distribution and the registration observations, the catheter tip position is tracked in the 3D vessel tree using registrations to the 2D fluoroscopic images. The method is evaluated on simulated data and two clinical sequences. In the simulations, we obtain a median tip position accuracies up to 2.9  mm. On clinical sequence, the distance between the catheter and the projected vessels after registration is below 1.9 mm.


Catheter Tip Tracking Rigid Registration Guidance X-ray Fluoroscopy 3DRA Hidden markov model Abdominal TACE Liver Breathing 



This research is funded by Philips Healthcare, Best, The Netherlands.

Supplementary material

371121_1_En_5_MOESM1_ESM.avi (4.5 mb)
Supplementary material (avi 4,656 KB)


  1. 1.
    Ambrosini, P., Ruijters, D., Niessen, W.J., Moelker, A., van Walsum, T.: Continuous roadmapping in liver tace procedures using 2d–3d catheter-based registration. Int. J. Comput. Assist. Radiol. Surg. 1–14 (2015)Google Scholar
  2. 2.
    Atasoy, S., Groher, M., Zikic, D., Glocker, B., Waggershauser, T., Pfister, M., Navab, N.: Real-time respiratory motion tracking: roadmap correction for hepatic artery catheterizations. In: Medical imaging, pp. 691815–691815. International Society for Optics and Photonics (2008)Google Scholar
  3. 3.
    Groher, M., Zikic, D., Navab, N.: Deformable 2d–3d registration of vascular structures in a one view scenario. IEEE Trans. Med. Imaging 28(6), 847–860 (2009)CrossRefGoogle Scholar
  4. 4.
    Heibel, H., Glocker, B., Groher, M., Pfister, M., Navab, N.: Interventional tool tracking using discrete optimization. IEEE Trans. Med. Imaging 32(3), 544–555 (2013)CrossRefGoogle Scholar
  5. 5.
    Liao, R., Zhang, L., Sun, Y., Miao, S., Chefd’Hotel, C.: A review of recent advances in registration techniques applied to minimally invasive therapy. IEEE Trans. Multimedia 15(5), 983–1000 (2013)CrossRefGoogle Scholar
  6. 6.
    Ma, Y., King, A.P., Gogin, N., Gijsbers, G., Rinaldi, C., Gill, J., Razavi, R., Rhode, K.S.: Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures. IEEE Trans. Biomed. Eng. 59(1), 122–131 (2012)CrossRefGoogle Scholar
  7. 7.
    Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3d/2d registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)CrossRefGoogle Scholar
  8. 8.
    Mitrović, U., Spiclin, Z., Likar, B., Pernuš, F.: 3d–2d registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans. Med. Imaging 32(8), 1550–1563 (2013)CrossRefGoogle Scholar
  9. 9.
    Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  10. 10.
    Rivest-Henault, D., Sundar, H., Cheriet, M.: Nonrigid 2d/3d registration of coronary artery models with live fluoroscopy for guidance of cardiac interventions. IEEE Trans. Med. Imaging 31(8), 1557–1572 (2012)CrossRefGoogle Scholar
  11. 11.
    Ruijters, D., Homan, R., Mielekamp, P., Van de Haar, P., Babic, D.: Validation of 3d multimodality roadmapping in interventional neuroradiology. Phys. Med. Biology 56(16), 5335 (2011)CrossRefGoogle Scholar
  12. 12.
    Selle, D., Preim, B., Schenk, A., Peitgen, H.O.: Analysis of vasculature for liver surgical planning. IEEE Trans. Med. Imaging 21(11), 1344–1357 (2002)CrossRefGoogle Scholar
  13. 13.
    Van Walsum, T., Baert, S.A., Niessen, W.J.: Guide wire reconstruction and visualization in 3dra using monoplane fluoroscopic imaging. IEEE Trans. Med. Imaging 24(5), 612–623 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pierre Ambrosini
    • 1
    Email author
  • Ihor Smal
    • 1
  • Daniel Ruijters
    • 2
  • Wiro J. Niessen
    • 1
    • 4
  • Adriaan Moelker
    • 3
  • Theo van Walsum
    • 1
  1. 1.Biomedical Imaging Group Rotterdam, Department of Radiology and Medical InformaticsErasmus MCRotterdamThe Netherlands
  2. 2.Philips Healthcare, Interventional X-ray InnovationBestThe Netherlands
  3. 3.Department of RadiologyErasmus MCRotterdamThe Netherlands
  4. 4.Imaging Science and Technology, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands

Personalised recommendations