  Cardiovascular disease (CVD) is the global leading cause of death. A strong
risk factor for CVD events is the amount of coronary artery calcium (CAC). To
meet demands of the increasing interest in quantification of CAC, i.e. coronary
calcium scoring, especially as an unrequested finding for screening and
research, automatic methods have been proposed. Current automatic calcium
scoring methods are relatively computationally expensive and only provide
scores for one type of CT. To address this, we propose a computationally
efficient method that employs two ConvNets: the first performs registration to
align the fields of view of input CTs and the second performs direct regression
of the calcium score, thereby circumventing time-consuming intermediate CAC
segmentation. Optional decision feedback provides insight in the regions that
contributed to the calcium score. Experiments were performed using 903 cardiac
CT and 1,687 chest CT scans. The method predicted calcium scores in less than
0.3 s. Intra-class correlation coefficient between predicted and manual calcium
scores was 0.98 for both cardiac and chest CT. The method showed almost perfect
agreement between automatic and manual CVD risk categorization in both
datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93
in chest CT. Performance is similar to that of state-of-the-art methods, but
the proposed method is hundreds of times faster. By providing visual feedback,
insight is given in the decision process, making it readily implementable in
clinical and research settings.
