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An Automated Segmentation and Classification Framework for CT-Based Myocardial Perfusion Imaging for Detecting Myocardial Perfusion Defect

  • Zhen Qian
  • Parag Joshi
  • Sarah Rinehart
  • Szilard Voros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)

Abstract

Thanks to the recent development of the high-resolution and high-speed multi-sliced CT, CT-based perfusion imaging has become possible. In this paper, we have developed a 320-MDCT-based perfusion imaging framework to detect myocardial ischemia. We designed a rest/stress perfusion imaging protocol, developed an automated LV segmentation algorithm, and adapted a LDA-based classifier to predict myocardial ischemia using the intensity profiles in rest perfusion images. Experiments were done on 6 stress/rest CT perfusion data sets from patients with obstructive coronary artery disease (CAD) and 6 rest CT perfusion data sets from normal subjects. Experimental results have shown that rest perfusion images have the potential of accurately predicting ischemia caused by obstructive CAD.

Keywords

Myocardial Perfusion Myocardial Perfusion Imaging Perfusion Imaging Linear Discriminant Analysis Obstructive Coronary Artery Disease 
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

  • Zhen Qian
    • 1
  • Parag Joshi
    • 1
  • Sarah Rinehart
    • 1
  • Szilard Voros
    • 1
  1. 1.Piedmont Heart InstitutePiedmont HealthcareAtlantaUSA

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