title: Quantitation of absolute area of a coronary arterial stenosis: experimental validation with a preparation in vivo.
abstract: The absolute cross-sectional area of a coronary stenosis measured by quantitative coronary angiography correlates well with its hemodynamic significance. We evaluated a combined approach using edge detection applied to the normal segment and videodensitometry applied to the stenosis to determine the absolute cross-sectional area of the stenosis (videodensity method). The results were then compared with those with the edge detection method applied directly to the stenosis. The area of the stenosis by the edge detection method was calculated by analyzing two orthogonal projections for irregular stenoses and with use of the formula for the area of an ellipse (ellipse method). The accuracy of both these techniques was assessed by analyzing digital angiograms acquired from closed-chest dogs in which 10 plastic cylinders with precisely machined circular and irregular lumina were inserted into the coronary arteries. Angiograms of irregular stenoses were acquired in two orthogonal views. The ellipse method applied to circular stenoses was very accurate, with r = .97, average absolute difference (AAD) = 0.21 mm2, and SEE = 0.30. For the videodensity method r = .97, AAD = 0.84 mm2, and SEE = 0.40. Irregular stenoses were better quantitated by the videodensity method applied in one view (AAD = 0.50 mm2, SEE = 0.47) than by the ellipse method applied in two orthogonal projections (AAD = 1.03 mm2, SEE = 0.87). Overall, the two methods were comparable in accuracy (for videodensity, AAD = 0.65 mm2, SEE = 0.71 vs AAD = 0.54 mm2, SEE = 0.79 for ellipse).(ABSTRACT TRUNCATED AT 250 WORDS)
mesh_terms: D000792:Angiography / Q000379:methods*; D000818:Animals; D003251:Constriction, Pathologic; D017023:Coronary Angiography; D003327:Coronary Disease / Q000000981:diagnostic imaging* / Q000473:pathology; D003331:Coronary Vessels / Q000473:pathology; D003720:Densitometry / Q000379:methods*; D004285:Dogs; D007091:Image Processing, Computer-Assisted