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Coronary artery calcium scoring: Can we do better?
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Paper Abstract

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) that can be quantified in CT scans showing the heart. CAC lesions are defined as lesions in the coronary arteries with image intensity above 130 HU. The use of a threshold may lead to under- or over-estimation of the amount of CAC and, hence, to incorrect cardiovascular categorization of patients. This is especially pronounced in CT scans without ECG-synchronization where lesions are more subject to cardiac motion and partial volume effects. To address this, we propose a method for quantification of CAC without a threshold. A set of 373 cardiac and 1181 chest CT scans was included to develop the method and a set of 21 scan-rescan pairs (42 scans) was included for final evaluation. Assuming that the attenuation of CAC is superimposed on the attenuation of the artery, we aimed to separate the CAC from the coronary arteries by employing a CycleGAN to generate a synthetic image without CAC from an image containing CAC and vice versa. By subtracting the synthetic image without CAC from the image with CAC, a CAC map is created. The CAC-map can subsequently be used to identify and quantify CAC. The ground truth, i.e. the true amount of CAC, can not be established, therefore, in this work the results generated by the method are compared with clinical calcium scoring in terms of reproducibility. The average relative difference between the calcium scores in scan-rescan pairs of scans was 50% with the proposed method and 86% for the conventional method. Moreover, the correlation between CAC pseudo masses in scan-rescan pairs was 0.92 with the proposed method and 0.89 with conventional calcium scoring. Our proposed method is able to identify and quantify CAC lesions in CT scans without using an intensity level thresholding. This might allow for more reproducible quantification of CAC in CT scans made without ECG synchronization, and, therefore, it might allow more accurate CVD risk prediction.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130G (10 March 2020); doi: 10.1117/12.2549557
Show Author Affiliations
Sanne G. M. Van velzen, Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Utrecht Univ. (Netherlands)
Bob D. de Vos, Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Utrecht Univ. (Netherlands)
Helena M. Verkooijen, Univ. Medical Ctr. Utrecht (Netherlands)
Tim Leiner, Univ. Medical Ctr. Utrecht (Netherlands)
Max A. Viergever, Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Utrecht Univ. (Netherlands)
Ivana Išgum, Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Amsterdam Univ. Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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