  Purpose: Coronary artery calcium (CAC) score, i.e. the amount of CAC
quantified in CT, is a strong and independent predictor of coronary heart
disease (CHD) events. However, CAC scoring suffers from limited interscan
reproducibility, which is mainly due to the clinical definition requiring
application of a fixed intensity level threshold for segmentation of
calcifications. This limitation is especially pronounced in
non-ECG-synchronized CT where lesions are more impacted by cardiac motion and
partial volume effects. Therefore, we propose a CAC quantification method that
does not require a threshold for segmentation of CAC. Approach: Our method
utilizes a generative adversarial network where a CT with CAC is decomposed
into an image without CAC and an image showing only CAC. The method, using a
CycleGAN, was trained using 626 low-dose chest CTs and 514 radiotherapy
treatment planning CTs. Interscan reproducibility was compared to clinical
calcium scoring in radiotherapy treatment planning CTs of 1,662 patients, each
having two scans. Results: A lower relative interscan difference in CAC mass
was achieved by the proposed method: 47% compared to 89% manual clinical
calcium scoring. The intraclass correlation coefficient of Agatston scores was
0.96 for the proposed method compared to 0.91 for automatic clinical calcium
scoring. Conclusions: The increased interscan reproducibility achieved by our
method may lead to increased reliability of CHD risk categorization and
improved accuracy of CHD event prediction.
