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.
Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD. Existing methods analyzing CT images from lung cancer screening for prediction of CVD events or mortality use engineered features extracted from the images combined with patient information. In this work we propose a method that automatically predicts 5-year cardiovascular mortality directly from chest CT scans without the need for hand-crafting image features. A set of 1,583 participants of the National Lung Screening Trial was included (1,188 survivors, 395 nonsurvivors). Low-dose chest CT images acquired at baseline were analyzed and the follow-up time was 5 years. To limit the analysis to the heart region, the heart was first localized by our previously developed algorithm for organ localization exploiting convolutional neural networks. Thereafter, a convolutional autoencoder was used to encode the identified heart region. Finally, based on the extracted encodings subjects were classified into survivors or non-survivors using a neural network. The performance of the method was assessed in eight cross-validation experiments with 1,433 images used for training, 50 for validation and 100 for testing. The method achieved a performance with an area under the ROC curve of 0.73. The results demonstrate that prediction of cardiovascular mortality directly from low-dose screening chest CT scans, without hand-crafted features, is feasible, allowing identification of subjects at risk of fatal CVD events.