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.
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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: Metaxas, D.N., Axel, L. (eds) Functional Imaging and Modeling of the Heart. FIMH 2011. Lecture Notes in Computer Science, vol 6666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21028-0_26
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DOI: https://doi.org/10.1007/978-3-642-21028-0_26
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