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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modeling of the Heart - 6th International Conference, FIMH 2011, Proceedings
Pages206-214
Number of pages9
DOIs
StatePublished - Jun 2 2011
Event6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011 - New York City, NY, United States
Duration: May 25 2011May 27 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011
CountryUnited States
CityNew York City, NY
Period5/25/115/27/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'An automated segmentation and classification framework for CT-based myocardial perfusion imaging for detecting myocardial perfusion defect'. Together they form a unique fingerprint.

  • Cite this

    Qian, Z., Joshi, P., Rinehart, S., & Voros, S. (2011). An automated segmentation and classification framework for CT-based myocardial perfusion imaging for detecting myocardial perfusion defect. In Functional Imaging and Modeling of the Heart - 6th International Conference, FIMH 2011, Proceedings (pp. 206-214). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6666 LNCS). https://doi.org/10.1007/978-3-642-21028-0_26