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Jordan Liu

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Proceedings Article | 20 March 2015 Paper

Automatic selection of best quality vessels from multiple-phase coronary CT angiography (cCTA)

Jordan Liu, Lubomir Hadjiiski, Heang-Ping Chan, Chuan Zhou, Jun Wei, Aamer Chughtai, Jean Kuriakose, Prachi Agarwal, Ella Kazerooni

Proceedings Volume 9414, 94140E (2015) https://doi.org/10.1117/12.2082637

KEYWORDS: Computer aided diagnosis and therapy, Visualization, Image segmentation, Composites, Arteries, Quality measurement, Image quality, Angiography, Radiology, CAD systems

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We are developing an automated method to select the best-quality vessels from coronary arterial trees in multiplephase cCTA and build a best-quality tree to facilitate the detection of stenotic plaques. Using our previously developed vessel registration method, the vessels from different phases were automatically registered. Branching points on the centerline are projected onto the registered trees. The centerlines are split into branches based on the projected branching points. Each tree branch is then straightened. The registered trees and centerline branches are used to determine the correspondence of branches between phases so that each branch can be compared to its corresponding branches in the other phases. A vessel quality measure (VQM) is calculated as the average radial gradients at the vessel wall over the entire vessel branch. The quality of the corresponding branches from all phases is automatically compared using the VQM. An observer preference study was conducted with two radiologists to visually compare the quality of the vessels. For each branch, the pair that was automatically determined to be the best and worst quality by the VQM was used for the radiologists’ visual assessment. Each radiologist, blinded to the VQM, evaluated pairs of corresponding branches and provided their preference. The performance of the automatic selection using VQM was evaluated as the percentage of the total number of vessel pairs for which the automatic selection agreed with the radiologist’s selection of the higher quality branch in the pair. The agreement between the first radiologist and the automated selection was 80% and that between the second radiologist and the automated selection was 82%. In comparison, the agreement between the two radiologists was 90%. This preliminary study demonstrates the feasibility of using an automated method to select the best-quality vessels from multiple cCTA phases.

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