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An automatic method for the identification and quantification of myocardial perfusion defects or infarction from cardiac CT images

The current study presents an automatic algorithm for detection of myocardial infarction and ischemia using cardiac CT image data. The classification is based on probabilistic tissue modeling, where a pixel is classified according to its maximum a-posteriori probability (MAP) as belonging to a normal or abnormal tissue segment. The pixels are represented in a two-dimensional space, where the first dimension is based on pixel intensity and the second relates to pixel position in the radial (transmural) direction. By means of this method, optimal thresholds for separating abnormal from normal pixels are calculated and clusters of abnormal pixels are identified. The method's performance was evaluated in comparison to an expert analysis of the cardiac CT images and showed good agreement.