Catheter Tracking: Filter-Based vs. Learning-Based

  • Alexander Brost
  • Andreas Wimmer
  • Rui Liao
  • Joachim Hornegger
  • Norbert Strobel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)


Atrial fibrillation is the most common sustained arrhythmia. One important treatment option is radio-frequency catheter ablation (RFCA) of the pulmonary veins attached to the left atrium. RFCA is usually performed under fluoroscopic (X-ray) image guidance. Overlay images computed from pre-operative 3-D volumetric data can be used to add anatomical detail otherwise not visible under X-ray. Unfortunately, current fluoro overlay images are static, i.e., they do not move synchronously with respiratory and cardiac motion. A filter-based catheter tracking approach using simultaneous biplane fluoroscopy was previously presented. It requires localization of a circumferential tracking catheter, though. Unfortunately, the initially proposed method may fail to accommodate catheters of different size. It may also detect wrong structures in the presence of high background clutter. We developed a new learning-based approach to overcome both problems. First, a 3-D model of the catheter is reconstructed. A cascade of boosted classifiers is then used to segment the circumferential mapping catheter. Finally, the 3-D motion at the site of ablation is estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. We compared our method to the previous approach using 13 clinical data sets and found that the 2-D tracking error improved from 1.0 mm to 0.8 mm. The 3-D tracking error was reduced from 0.8 mm to 0.7 mm.


Pulmonary Vein Tracking Error Motion Compensation Mapping Catheter Overlay Image 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Brost
    • 1
  • Andreas Wimmer
    • 1
  • Rui Liao
    • 2
  • Joachim Hornegger
    • 1
  • Norbert Strobel
    • 3
  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander-University of Erlangen-NurembergErlangenGermany
  2. 2.Siemens Corporate ResearchPrincetonUSA
  3. 3.Siemens AGForchheimGermany

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