An automated segmentation and classification framework for CT-based myocardial perfusion imaging for detecting myocardial perfusion defect

Authors:
Zhen Qian;Parag Joshi;Sarah Rinehart;Szilard Voros
Affiliations:
Piedmont Heart Institute, Piedmont Healthcare, Atlanta, GA;Piedmont Heart Institute, Piedmont Healthcare, Atlanta, GA;Piedmont Heart Institute, Piedmont Healthcare, Atlanta, GA;Piedmont Heart Institute, Piedmont Healthcare, Atlanta, GA
Venue:
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Year:
2011

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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.