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Proceedings Paper

Automated coronary artery calcium scoring from non-contrast CT using a patient-specific algorithm
Author(s): Xiaowei Ding; Piotr J. Slomka; Mariana Diaz-Zamudio; Guido Germano; Daniel S. Berman; Demetri Terzopoulos; Damini Dey
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Paper Abstract

Non-contrast cardiac CT is used worldwide to assess coronary artery calcium (CAC), a subclinical marker of coronary atherosclerosis. Manual quantification of regional CAC scores includes identifying candidate regions, followed by thresholding and connected component labeling. We aimed to develop and validate a fully-automated, algorithm for both overall and regional measurement of CAC scores from non-contrast CT using a hybrid multi-atlas registration, active contours and knowledge-based region separation algorithm. A co-registered segmented CT atlas was created from manually segmented non-contrast CT data from 10 patients (5 men, 5 women) and stored offline. For each patient scan, the heart region, left ventricle, right ventricle, ascending aorta and aortic root are located by multi-atlas registration followed by active contours refinement. Regional coronary artery territories (left anterior descending artery, left circumflex artery and right coronary artery) are separated using a knowledge-based region separation algorithm. Calcifications from these coronary artery territories are detected by region growing at each lesion. Global and regional Agatston scores and volume scores were calculated in 50 patients. Agatston scores and volume scores calculated by the algorithm and the expert showed excellent correlation (Agatston score: r = 0.97, p < 0.0001, volume score: r = 0.97, p < 0.0001) with no significant differences by comparison of individual data points (Agatston score: p = 0.30, volume score: p = 0.33). The total time was <60 sec on a standard computer. Our results show that fast accurate and automated quantification of CAC scores from non-contrast CT is feasible.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132U (20 March 2015); doi: 10.1117/12.2081633
Show Author Affiliations
Xiaowei Ding, Cedars-Sinai Medical Ctr. (United States)
Univ. of California, Los Angeles (United States)
Piotr J. Slomka, Univ. of California, Los Angeles (United States)
Mariana Diaz-Zamudio, Cedars-Sinai Medical Ctr. (United States)
Guido Germano, Univ. of California, Los Angeles (United States)
Daniel S. Berman, Cedars-Sinai Medical Ctr. (United States)
Demetri Terzopoulos, Univ. of California, Los Angeles (United States)
Damini Dey, Cedars-Sinai Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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