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

  • Gozde Gul Isguder
  • Gozde Unal
  • Martin Groher
  • Nassir Navab
  • Ali Kemal Kalkan
  • Muzaffer Degertekin
  • Holger Hetterich
  • Johannes Rieber
Conference paper

DOI: 10.1007/978-3-642-15699-1_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)
Cite this paper as:
Isguder G.G. et al. (2010) Manifold Learning for Image-Based Gating of Intravascular Ultrasound(IVUS) Pullback Sequences. In: Liao H., Edwards P.J.., Pan X., Fan Y., Yang GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gozde Gul Isguder
    • 1
  • Gozde Unal
    • 1
  • Martin Groher
    • 2
  • Nassir Navab
    • 2
  • Ali Kemal Kalkan
    • 3
  • Muzaffer Degertekin
    • 3
  • Holger Hetterich
    • 4
  • Johannes Rieber
    • 4
  1. 1.Sabanci University 
  2. 2.Technical University Of Munich 
  3. 3.Yeditepe University Hospital 
  4. 4.Ludwig Maximilian University Hospital 

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