Model-driven visualization of coronary arteries.
In a joint project between the Department of Computer and Information Sciences and the Department of Radiology, we are applying techniques of artificial intelligence to improve clinical performance in coronary arteries. Specifically, we are investigating how images from intravenous digital subtraction angiography (DSA) can be enhanced so that their efficacy for lesion detection and quantitation becomes comparable with that of the more dangerous procedure of selective coronary arteriography. The enhancement techniques (which include algorithms for 3-dimensional vessel detection, reconstruction and display, as well as for accurate lumen-size estimation) are based on models of (i) the 3-dimensional topological structure of the coronary arterial tree, (ii) myocardial dynamics, and (iii) the X-ray imaging process involved in producing digital subtraction angiograms. The evaluation of these model-driven visualization techniques is done by the standard psychophysical method of Receiver Operating Characteristic (ROC) analysis applied to observer performance tests on images from an animal coronary atherosclerosis model.
D000703:Analog-Digital Conversion; D000792:Angiography / Q000295:instrumentation / Q000379:methods*; D000818:Animals; D003201:Computers; D017023:Coronary Angiography; D003327:Coronary Disease / Q000000981:diagnostic imaging; D003626:Data Display; D006801:Humans; D008954:Models, Biological; D011601:Psychophysics