Chapter

Medical Imaging and Augmented Reality

Volume 6326 of the series Lecture Notes in Computer Science pp 139-148

Manifold Learning for Image-Based Gating of Intravascular Ultrasound(IVUS) Pullback Sequences

  • Gozde Gul IsguderAffiliated withSabanci University
  • , Gozde UnalAffiliated withSabanci University
  • , Martin GroherAffiliated withTechnical University Of Munich
  • , Nassir NavabAffiliated withTechnical University Of Munich
  • , Ali Kemal KalkanAffiliated withYeditepe University Hospital
  • , Muzaffer DegertekinAffiliated withYeditepe University Hospital
  • , Holger HetterichAffiliated withLudwig Maximilian University Hospital
  • , Johannes RieberAffiliated withLudwig Maximilian University Hospital

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Abstract

Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel structures. The IVUS frames are acquired by pulling the catheter back with a motor running at a constant speed. However, during the pullback, some artifacts occur due to the beating heart. These artifacts cause inaccurate measurements for total vessel and lumen volume and limitation for further processing. Elimination of these artifacts are possible with an ECG (electrocardiogram) signal, which determines the time interval corresponding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of important information about the vessel, and furthermore, ECG gating function may not be available in all clinical systems. To address this problem, we propose an image-based gating technique based on manifold learning. Quantitative tests are performed on 3 different patients, 6 different pullbacks and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method.