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Proceedings Paper

Quantitative analysis of arterial flow properties for detection of non-calcified plaques in ECG-gated coronary CT angiography
Author(s): Jun Wei; Chuan Zhou; Heang-Ping Chan; Aamer Chughtai; Prachi Agarwal; Jean Kuriakose; Lubomir Hadjiiski; Smita Patel; Ella Kazerooni
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Paper Abstract

We are developing a computer-aided detection system to assist radiologists in detection of non-calcified plaques (NCPs) in coronary CT angiograms (cCTA). In this study, we performed quantitative analysis of arterial flow properties in each vessel branch and extracted flow information to differentiate the presence and absence of stenosis in a vessel segment. Under rest conditions, blood flow in a single vessel branch was assumed to follow Poiseuille’s law. For a uniform pressure distribution, two quantitative flow features, the normalized arterial compliance per unit length (Cu) and the normalized volumetric flow (Q) along the vessel centerline, were calculated based on the parabolic Poiseuille solution. The flow features were evaluated for a two-class classification task to differentiate NCP candidates obtained by prescreening as true NCPs and false positives (FPs) in cCTA. For evaluation, a data set of 83 cCTA scans was retrospectively collected from 83 patient files with IRB approval. A total of 118 NCPs were identified by experienced cardiothoracic radiologists. The correlation between the two flow features was 0.32. The discriminatory ability of the flow features evaluated as the area under the ROC curve (AUC) was 0.65 for Cu and 0.63 for Q in comparison with AUCs of 0.56-0.69 from our previous luminal features. With stepwise LDA feature selection, volumetric flow (Q) was selected in addition to three other luminal features. With FROC analysis, the test results indicated a reduction of the FP rates to 3.14, 1.98, and 1.32 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. The study indicated that quantitative blood flow analysis has the potential to provide useful features for the detection of NCPs in cCTA.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141H (20 March 2015); doi: 10.1117/12.2082367
Show Author Affiliations
Jun Wei, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Aamer Chughtai, Univ. of Michigan (United States)
Prachi Agarwal, Univ. of Michigan (United States)
Jean Kuriakose, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Smita Patel, Univ. of Michigan (United States)
Ella Kazerooni, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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