AI Article Synopsis
- - This project collaborates between Computer and Information Sciences and Radiology to use artificial intelligence to enhance the effectiveness of intravenous digital subtraction angiography (DSA) for detecting issues in coronary arteries.
- - The focus is on developing algorithms for better 3D visualization and lumen-size estimation, with models that take into account the coronary artery structure and X-ray imaging dynamics.
- - The effectiveness of these innovations is evaluated through Receiver Operating Characteristic (ROC) analysis in tests using images from an animal model of coronary atherosclerosis.
Article Abstract
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.
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