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• W2201461185 abstract "Clinical decisions regarding diagnosis and effective treatment of coronary artery disease frequently require integration of information from various imaging modalities, which are acquired, processed, and read at different physical locations and times. We have developed methods to integrate the information in 2 cardiac imaging studies, perfusion SPECT and coronary angiography. Three-dimensional (3D) models of the coronary artery tree created from biplane angiograms were automatically aligned with 3D models of the left ventricular epicardial surface created from perfusion SPECT. Myocardial mass at risk was used as a unique measure to validate the accuracy of the unification.Thirty patients were injected with the perfusion agent (99m)Tc-tetrosfosmin during balloon occlusion while undergoing percutaneous transluminal coronary angioplasty for single-vessel coronary artery disease. Thus, a single, severe perfusion defect was induced by a single coronary artery occlusion of known severity and placement. The accuracy of the unification was measured by computing the overlap between physiologic area at risk, determined using SPECT perfusion quantification techniques only, and anatomic area at risk, determined using coronary artery anatomy aligned with the epicardial surface of the left ventricle.The unification resulted in an 80% overlap of areas at risk, and an overlap of 84% of normal areas, for all coronary artery distributions. The mass at risk measured based on the unified anatomic information correlated with the physiologically based mass at risk as y = 0.92x + 10.3 g; r = 0.76, SEE = 10.4 g.A unification algorithm for automatically registering 3D models of the epicardial surface from perfusion SPECT and 3D coronary artery trees from coronary angiography has been presented and validated in 30 patient studies." @default.
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• W2201461185 date "2004-05-01" @default.
• W2201461185 modified "2023-10-18" @default.
• W2201461185 title "Three-dimensional fusion of coronary arteries with myocardial perfusion distributions: clinical validation." @default.
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