Model Driven Visualization of Coronary Arteries

Abstract

In a joint project between the Departments of Computer and Information Sciences and Radiology we are applying techniques of artificial intelligence to improve clinical performance as related to 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 three-dimensional vessel detection, reconstruction, and display, as well as for accurate lumen size estjmation) are based on models of (i) the three-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.