TY - GEN
T1 - Coronary artery calcium scoring
T2 - Medical Imaging 2020: Image Processing
AU - van Velzen, Sanne G.M.
AU - de Vos, Bob D.
AU - Verkooijen, Helena M.
AU - Leiner, Tim
AU - Viergever, Max A.
AU - Išgum, Ivana
N1 - Funding Information:
The authors gratefully acknowledge the Dutch Cancer Society for the financial support (NCT03206333). The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
Funding Information:
The authors gratefully acknowledge the Dutch Cancer Society for the financial support (NCT03206333). The authors thank the National Cancer Institute for access to NCI's data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
Publisher Copyright:
© 2020 SPIE. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092590938&partnerID=8YFLogxK
U2 - 10.1117/12.2549557
DO - 10.1117/12.2549557
M3 - Conference contribution
AN - SCOPUS:85092590938
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
PB - SPIE
Y2 - 17 February 2020 through 20 February 2020
ER -