Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool

The purpose of this study was to investigate a 3D coronary artery segmentation algorithm using 16-row MDCT data sets. Fifty patients underwent cardiac CT (Sensation 16, Siemens) and coronary angiography. Automatic and manual detection of coronary artery stenosis was performed. A 3D coronary artery segmentation algorithm (Fraunhofer Institute for Computer Graphics, Darmstadt) was used for automatic evaluation. All significant stenoses (>50%) in vessels >1.5 mm in diameter were protocoled. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of coronary angiography. Sensitivity and specificity were determined for automatic and manual detection as well as was the time for both CT-based evaluation methods. The overall sensitivity and specificity of the automatic and manual approach were 93.1 vs. 95.83% and 86.1 vs. 81.9%. The time required for automatic evaluation was significantly shorter than with the manual approach, i.e., 246.04+/-43.17 s for the automatic approach and 526.88+/-45.71 s for the manual approach (P