In vivo validation of an experimental adaptive quantitative coronary angiography algorithm to circumvent overestimation of small luminal diameters.
The reliability of quantitative coronary angiography (QCA) measurements is of fundamental importance for the study and practice of interventional cardiology. In vivo validation results have consistently reported a tendency for QCA systems to overestimate small luminal diameters. Such a systematic error may result in the underestimation of luminal gain during intracoronary procedures and in the underestimation of progression of coronary artery disease during longitudinal studies. We report the in vivo validation results of an experimental adaptive edge-detection algorithm that was developed to reduce overestimation of small luminal diameters by incorporating a dynamic function of variable kernel size of the derivative operator and variable weighting of the first and second derivatives of the brightness profile. The results of the experimental algorithm were compared to those of the conventional parent edge detection algorithm with fixed parameters. Dynamic adjustment of the edge-detection algorithm parameters was found to improve measurements of small (< 0.8-mm) luminal diameters as evidenced by an intercept of +.07 mm for the algorithm with variable weighting compared to +0.21 mm for the parent algorithm with fixed weighting. A slope of < 1 was found for both the parent and experimental algorithms with subsequent underestimation of large luminal diameters. Systematic errors in a QCA system can be identified and corrected by the execution of objective in vivo validation studies and the consequent refinement of edge-detection algorithms. The overestimation of small luminal diameters may be overcome by the incorporation of a dynamic edge-detection algorithm.(ABSTRACT TRUNCATED AT 250 WORDS)
D000465:Algorithms; D017023:Coronary Angiography / Q000295:instrumentation*; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D006801:Humans; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation*