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6DoF Catheter Detection, Application to Intracardiac Echocardiography

  • Kristóf Ralovich
  • Matthias John
  • Estelle Camus
  • Nassir Navab
  • Tobias Heimann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Hybrid imaging systems, consisting of fluoroscopy and echocardiography, are increasingly selected for intra-operative support of minimally invasive cardiac interventions. Intracardiac echocardiograpy (ICE) is an emerging modality with the promise of removing sedation or general anesthesia associated with transesophageal echocardiography (TEE).

We introduce a novel 6 degrees of freedom (DoF) pose estimation approach for catheters (equipped with radiopaque ball markers) in single X-Ray fluoroscopy projection and investigate the method’s application to a prototype ICE catheter. Machine learning based catheter detection is implemented in a Bayesian hypothesis fusion framework, followed by refinement of ball marker locations through template matching. Marker correspondence and 3D pose estimation are solved through iterative optimization.

The method registers the ICE volume to the C-arm coordinate system. Experiments are performed on synthetic and porcine in-vivo data. Target registration error (TRE), defined in the echo cone, is the basis of our preliminary evaluation. The method reached 8.06±7.2 mm TRE on 703 cases.

Potential uses of our hybrid system include structural heart disease interventions and electrophysiologycal mapping or catheter ablation procedures.

Keywords

Transcatheter Aortic Valve Implantation Target Registration Error Digitally Reconstruct Radiograph Hypothesis Fusion Ball Marker 
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.

References

  1. 1.
    David, P., Dementhon, D., Duraiswami, R., Samet, H.: Softposit: Simultaneous pose and correspondence determination. IJCV 59, 259–284 (2004)CrossRefGoogle Scholar
  2. 2.
    Dementhon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. Int. J. Comput. Vision 15(1-2), 123–141 (1995)CrossRefGoogle Scholar
  3. 3.
    Garro, V., Crosilla, F., Fusiello, A.: Solving the pnp problem with anisotropic orthogonal procrustes analysis. In: 2011 3DIMPVT, pp. 262–269 (2012)Google Scholar
  4. 4.
    Hoffmann, K.R., Esthappan, J.: Determination of three-dimensional positions of known sparse objects from a single projection. Medical Physics (1997)Google Scholar
  5. 5.
    Lang, P., Seslija, P., Chu, M., Bainbridge, D., Guiraudon, G., Jones, D., Peters, T.: Us-fluoroscopy registration for transcatheter aortic valve implantation. IEEE Transactions on Biomedical Engineering 59(5), 1444–1453 (2012)CrossRefGoogle Scholar
  6. 6.
    Lu, X., Chen, T., Comaniciu, D.: Robust discriminative wire structure modeling with application to stent enhancement in fluoroscopy. In: CVPR 2011, p. 1121 (2011)Google Scholar
  7. 7.
    Ma, Y., Gogin, N., Cathier, P., Housden, R., Gijsbers, G., Cooklin, M., O’Neill, M., Gill, J., Rinaldi, C., Razavi, R., Rhode, K.: Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions. Medical Physics 40(7) (2013)Google Scholar
  8. 8.
    Ma, Y., Penney, G.P., Bos, D., Frissen, P., Rinaldi, C.A., Razavi, R., Rhode, K.S.: Hybrid echo and x-ray image guidance for cardiac catheterization procedures by using a robotic arm: a feasibility study. Physics in Medicine and Biology (2010)Google Scholar
  9. 9.
    Mountney, P., Ionasec, R., Kaizer, M., Mamaghani, S., Wu, W., Chen, T., John, M., Boese, J., Comaniciu, D.: Ultrasound and fluoroscopic images fusion by autonomous ultrasound probe detection. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 544–551. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: ICCV 2005, vol. 2, pp. 1589–1596. IEEE (2005)Google Scholar
  11. 11.
    Wang, P., Chen, T., Ecabert, O., Prummer, S., Ostermeier, M., Comaniciu, D.: Image-based device tracking for the co-registration of angiography and intravascular ultrasound images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 161–168. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Webster, B.: Cartosound module soundstar catheter (February 2014), http://www.biosensewebster.com/cartosound.php

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kristóf Ralovich
    • 1
    • 2
  • Matthias John
    • 3
  • Estelle Camus
    • 4
  • Nassir Navab
    • 2
  • Tobias Heimann
    • 1
  1. 1.Imaging and Computer VisionSiemens AG, Corporate TechnologyGermany
  2. 2.Technical University of MunichGermany
  3. 3.Healthcare SectorSiemens AGForchheimGermany
  4. 4.Siemens Medical Solutions Inc.Mountain ViewUSA

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