Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

Abstract

We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist’s top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.