BACKGROUND: The angiographic view (AGV) image is a new post-processing method that is similar to conventional coronary angiography (CAG). The purpose of this study was to evaluate its accuracy for coronary stenosis detection by 64-detector row computed tomography (CT). METHODS AND RESULTS: CT evaluation results of 17 patients were compared with the results of invasive CAG on a coronary segment basis concerning the presence of stenoses>50% diameter reduction. All images of the 3 viewing methods (combination of conventional methods, AGV image alone, and axial images alone) were evaluated in consensus by 3 cardiovascular radiologists. Among 196 assessable segments, invasive CAG showed significant coronary artery stenoses in 44 segments. 43 of 44 lesions were detected with the AGV image, and absence of significant stenosis was correctly identified in 135 of 152 segments (sensitivity 98%; specificity 89%; accuracy 91%; positive predictive value 72%, negative predictive value 99%). The sensitivity of the AGV image was the same as that of conventional methods (98%). There was no significant difference in accuracy between the AGV image (91%) and conventional methods (94%). The accuracy of the AGV image was significantly higher than the axial images alone (78%). CONCLUSIONS: AGV image shows promise as a post-processing method for identifying coronary artery stenosis with high accuracy.
BACKGROUND: The angiographic view (AGV) image is a new post-processing method that is similar to conventional coronary angiography (CAG). The purpose of this study was to evaluate its accuracy for coronary stenosis detection by 64-detector row computed tomography (CT). METHODS AND RESULTS: CT evaluation results of 17 patients were compared with the results of invasive CAG on a coronary segment basis concerning the presence of stenoses>50% diameter reduction. All images of the 3 viewing methods (combination of conventional methods, AGV image alone, and axial images alone) were evaluated in consensus by 3 cardiovascular radiologists. Among 196 assessable segments, invasive CAG showed significant coronary artery stenoses in 44 segments. 43 of 44 lesions were detected with the AGV image, and absence of significant stenosis was correctly identified in 135 of 152 segments (sensitivity 98%; specificity 89%; accuracy 91%; positive predictive value 72%, negative predictive value 99%). The sensitivity of the AGV image was the same as that of conventional methods (98%). There was no significant difference in accuracy between the AGV image (91%) and conventional methods (94%). The accuracy of the AGV image was significantly higher than the axial images alone (78%). CONCLUSIONS: AGV image shows promise as a post-processing method for identifying coronary artery stenosis with high accuracy.