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Proceedings Paper

Automatic detection of significant and subtle arterial lesions from coronary CT angiography
Author(s): Dongwoo Kang; Piotr Slomka; Ryo Nakazato; Victor Y. Cheng; James K. Min; Debiao Li; Daniel S. Berman; C.-C. Jay Kuo; Damini Dey
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Paper Abstract

Visual analysis of three-dimensional (3D) Coronary Computed Tomography Angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. We aimed to develop an accurate, automated algorithm for detection of significant and subtle coronary artery lesions compared to expert interpretation. Our knowledge-based automated algorithm consists of centerline extraction which also classifies 3 main coronary arteries and small branches in each main coronary artery, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery, considering small branch locations. We applied this algorithm to 21 CCTA patient datasets, acquired with dual-source CT, where 7 datasets had 17 lesions with stenosis greater than or equal to 25%. The reference standard was provided by visual and quantitative identification of lesions with any ≥25% stenosis by an experienced expert reader. Our algorithm identified 16 out of the 17 lesions confirmed by the expert. There were 16 additional lesions detected (average 0.13/segment); 6 out of 16 of these were actual lesions with <25% stenosis. On persegment basis, sensitivity was 94%, specificity was 86% and accuracy was 87%. Our algorithm shows promising results in the high sensitivity detection and localization of significant and subtle CCTA arterial lesions.

Paper Details

Date Published: 14 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831435 (14 February 2012); doi: 10.1117/12.911659
Show Author Affiliations
Dongwoo Kang, Univ. of Southern California (United States)
Piotr Slomka, Cedars-Sinai Medical Ctr. (United States)
Ryo Nakazato, Cedars-Sinai Medical Ctr. (United States)
Victor Y. Cheng, Cedars-Sinai Medical Ctr. (United States)
James K. Min, Cedars-Sinai Medical Ctr. (United States)
Debiao Li, Cedars-Sinai Medical Ctr. (United States)
Daniel S. Berman, Cedars-Sinai Medical Ctr. (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)
Damini Dey, Cedars-Sinai Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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