With post-processing there was a significantly improved diagnostic image quality compared with non-processed data. In particular, similar contrast enhancement could be achieved with a reduced quantity of contrast medium injected during the CT acquisition.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.