Image-Based Device Tracking for the Co-registration of Angiography and Intravascular Ultrasound Images

  • Peng Wang
  • Terrence Chen
  • Olivier Ecabert
  • Simone Prummer
  • Martin Ostermeier
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)

Abstract

The accurate and robust tracking of catheters and transducers employed during image-guided coronary intervention is critical to improve the clinical workflow and procedure outcome. Image-based device detection and tracking methods are preferred due to the straightforward integration into existing medical equipments. In this paper, we present a novel computational framework for image-based device detection and tracking applied to the co-registration of angiography and intravascular ultrasound (IVUS), two modalities commonly used in interventional cardiology. The proposed system includes learning-based detections, model-based tracking, and registration using the geodesic distance. The system receives as input the selection of the coronary branch under investigation in a reference angiography image. During the subsequent pullback of the IVUS transducers, the system automatically tracks the position of the medical devices, including the IVUS transducers and guiding catheter tips, under fluoroscopy imaging. The localization of IVUS transducers and guiding catheter tips is used to continuously associate an IVUS imaging plane to the vessel branch under investigation. We validated the system on a set of 65 clinical cases, with high accuracy (mean errors less than 1.5mm) and robustness (98.46% success rate). To our knowledge, this is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.

Keywords

Intravascular Ultrasound Geodesic Distance Vessel Branch Breathing Motion IVUS 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 2011

Authors and Affiliations

  • Peng Wang
    • 1
  • Terrence Chen
    • 1
  • Olivier Ecabert
    • 2
  • Simone Prummer
    • 2
  • Martin Ostermeier
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchSiemens CorporationPrincetonUSA
  2. 2.Healthcare SectorSiemens AGForchheimGermany

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