Share Email Print
cover

Proceedings Paper

Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs
Author(s): Ivana Išgum; Bob D. de Vos; Jelmer M. Wolterink; Damini Dey; Daniel S. Berman; Mathieu Rubeaux; Tim Leiner; Piotr J. Slomka
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

CT attenuation correction (CTAC) images acquired with PET/CT visualize coronary artery calcium (CAC) and enable CAC quantification. CAC scores acquired with CTAC have been suggested as a marker of cardiovascular disease (CVD). In this work, an algorithm previously developed for automatic CAC scoring in dedicated cardiac CT was applied to automatic CAC detection in CTAC. The study included 134 consecutive patients undergoing 82-Rb PET/CT. Low-dose rest CTAC scans were acquired (100 kV, 11 mAs, 1.4mm×1.4mm×3mm voxel size). An experienced observer defined the reference standard with the clinically used intensity level threshold for calcium identification (130 HU). Five scans were removed from analysis due to artifacts. The algorithm extracted potential CAC by intensity-based thresholding and 3D connected component labeling. Each candidate was described by location, size, shape and intensity features. An ensemble of extremely randomized decision trees was used to identify CAC. The data set was randomly divided into training and test sets. Automatically identified CAC was quantified using volume and Agatston scores. In 33 test scans, the system detected on average 469mm3/730mm3 (64%) of CAC with 36mm3 false positive volume per scan. The intraclass correlation coefficient for volume scores was 0.84. Each patient was assigned to one of four CVD risk categories based on the Agatston score (0-10, 11-100, 101-400, <400). The correct CVD category was assigned to 85% of patients (Cohen's linearly weighted κ0.82). Automatic detection of CVD risk based on CAC scoring in rest CTAC images is feasible. This may enable large scale studies evaluating clinical value of CAC scoring in CTAC data.

Paper Details

Date Published: 21 March 2016
PDF: 6 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978405 (21 March 2016); doi: 10.1117/12.2216992
Show Author Affiliations
Ivana Išgum, Univ. Medical Ctr. Utrecht (Netherlands)
Bob D. de Vos, Univ. Medical Ctr. Utrecht (Netherlands)
Jelmer M. Wolterink, Univ. Medical Ctr. Utrecht (Netherlands)
Damini Dey, Cedars-Sinai Medical Ctr. (United States)
Daniel S. Berman, Cedars-Sinai Medical Ctr. (United States)
Mathieu Rubeaux, Cedars-Sinai Medical Ctr. (United States)
Tim Leiner, Univ. Medical Ctr. Utrecht (Netherlands)
Piotr J. Slomka, Cedars-Sinai Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top