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Non-contrast, cardiac CT Calcium Score (CTCS) images provide a low-cost cardiovascular disease screening exam to guide therapeutics. We are extending standard Agatston score to include cardiovascular risk assessments from features of epicardial adipose tissue, pericoronary adipose tissue, heart size, and more, which are currently extracted from Coronary CT Angiography (CCTA) images. To aid such determinations, we developed a deep-learning method to synthesize Virtual CT Angiography (VCTA) images from CTCS images. We retrospectively collected 256 patients who underwent CCTA and CTCS from our hospitals (MacKay and UH). Training on 205 patients from UH, we used the contrastive, unpaired translation method to create VCTA images. Testing on 51 patients from Mackay, we generated VCTA images that compared favorably to the matched CCTA images with enhanced coronaries and ventricular cavity that were well delineated from surrounding tissues (epicardial adipose tissue and myocardium). The automated segmentation of myocardium and left-ventricle cavity in VCTA showed strong agreement with the measurements obtained from CCTA. The measured percent volume differences between VCTA and CCTA segmentation were 2±8% for the myocardium and 5±10% for the left-ventricle cavity, respectively. Manually segmented coronary arteries from VCTA and CTCS (with guidance from registered CCTA) aligned well. Centerline displacements were within 50% of coronary artery diameter (4mm). Pericoronary adipose tissue measurements using the axial disk method showed excellent agreements between measurements from VCTA ROIs and manual segmentations (e.g., average HU differences were typically <3HU). Promising results suggest that VCTA can be used to add assessments indicative of cardiovascular risk from CTCS images.
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