A New Registration/Visualization Paradigm for CT-Fluoroscopy Guided RF Liver Ablation

  • Ruxandra Micu
  • Tobias F. Jakobs
  • Martin Urschler
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


2D-3D slice-to-volume registration for abdominal organs like liver is difficult due to the breathing motion and tissue deformation. The purpose of our approach is to ease CT-fluoroscopy (CT-fluoro) based needle insertion for the Radiofrequency Liver Ablation procedure using high resolution contrasted preoperative data. In this case, low signal-to-noise ratio, absence of contrast and additional presence of needle in CT-fluoro makes it difficult to guarantee the solution of any deformable slice-to-volume registration algorithm. In this paper, we first propose a method for creating a set of ground truth (GT) simulation data based on a non-linear deformation of the CT-fluoro volume obtained from real patients. Second, we split the CT-fluoro image and apply intensity based rigid and affine registration to each section. We then propose a novel solution, which consists of intuitive visualization sequences of optimal sub-volumes of preinterventional data based on the registration results. Experiments on synthetic and real patient data and direct feedback of two interventionalists validate our alternative approach.


Breathing Motion Registration Result Lower Plane Liver Motion Real Patient Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Friedman, M., Mikityansky, I., Kam, A., Libutti, S., Walther, M.M., Neeman, Z., Locklin, J.K., Wood, B.J.: Radiofrequency ablation of cancer. In: Cardio Vascular and Interventional Radiology (2004)Google Scholar
  2. 2.
    Xu, S., Fichtinger, G., Taylor, R.H., Cleary, K.R.: 3D motion tracking of pulmonary lesions using ct fluoroscopy images for robotically assisted lung biopsy. In: Galloway, R.L., Jr. (ed.) Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, vol. 5367, pp. 394–402 (2004)Google Scholar
  3. 3.
    Xu, S., Taylor, R., Fichtinger, G., Cleary, K.: Lung deformation estimation and four-dimensional ct lung reconstruction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 312–319. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Penney, G., Blackall, J., Hamady, M., Sabharwal, T., Adam, A., Hawkes, D.: Registration of freehand 3D ultrasound and magnetic resonance liver images. Medical Image Analysis 8, 81–91 (2004)CrossRefGoogle Scholar
  5. 5.
    Clifford, M., Banovac, F., Levy, E., Cleary, K.: Assessment of hepatic motion secondary to respiration for computer assisted interventions. Computer Aided Surgery 7, 291–299 (2002)CrossRefGoogle Scholar
  6. 6.
    Russakoff, D.B., Rohlfing, T., Ho, A., Kim, D.H., Shahidi, R., Adler Jr., J.R., Maurer Jr., C.R.: Evaluation of intensity-based 2D-3D spine image registration using clinical gold-standard data. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 151–160. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparision of similarity measures for use in 2D-3D medical image registration. IEEE Transactions on Medical Imaging (TMI) 17, 586–595 (1998)CrossRefGoogle Scholar
  8. 8.
    Ulzheimer, S., Leidecker, C.: Syngo explorer image reconstruction(ir) taskcard, bericht zur validierung der option ’addition von rauschen’. VAMP Verfahren und Apparate der Medizinischen Physik 8 (2004)Google Scholar
  9. 9.
    Urschler, M., Bischof, H.: Assessing breathing motion by shape matching of lung and diaphragm surfaces. In: Galloway Jr., R.L. (ed.) Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, pp. 440–452. SPIE (2005)Google Scholar
  10. 10.
    Wessling, J., Fischbach, R., Esseling, R., Raupach, R., Heindel, W.: Effect of dose reduction and noise reduction filters on detection of liver lesions using multi-detector row ct. In: RSNA (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruxandra Micu
    • 1
    • 2
  • Tobias F. Jakobs
    • 3
  • Martin Urschler
    • 4
  • Nassir Navab
    • 1
  1. 1.Chair for Computer Aided Medical Procedures (CAMP)TU MunichGermany
  2. 2.Computed TomographySiemens Medical SolutionsForchheim
  3. 3.Institute for Clinical RadiologyUniversity of Munich, Grosshadern Hospital 
  4. 4.Institute for Computer Graphics and VisionGraz University of Technology 

Personalised recommendations