Contrast-guided virtual monoenergetic image synthesis via adversarial learning for coronary CT angiography using photon counting detector CT

Coronary CT angiography (cCTA) is a non-invasive diagnostic test for coronary artery disease (CAD) that often faces challenges with dense calcifications and stents due to blooming artifacts, leading to stenosis overestimation. Virtual monoenergetic images (VMIs) from photon counting detector CT (PCD-CT) provide distinct clinical benefits. Lower keV VMIs enhance iodine and bone contrasts but struggle with blooming artifacts, while higher keV VMIs effectively reduce beam hardening, blooming, and metal artifacts but diminish contrast, presenting a trade-off among different keV levels. To address this, we introduce a contrast-guided virtual monoenergetic image synthesis framework (CITRINE) utilizing adversarial learning to synthesize images by integrating beneficial spectral characteristics from various keV levels. In this study, CITRINE is trained and validated with cardiac PCD-CT images using 100 keV and 70 keV VMIs as examples, showcasing its ability to synthesize images that combine the reduced blooming artifacts of 100 keV VMIs with the high contrast-to-noise features of 70 keV VMIs. CITRINE's performance was evaluated on three patient cCTA cases quantitatively and qualitatively in terms of image quality and assessments of percent diameter luminal stenosis. The synthesized images showed reduced blooming artifacts, similar to those observed at 100 keV VMI, and exhibited high iodine contrast in the coronary lumen, comparable to that of 70 keV VMI. Notably, compared to the original 70 keV VMI, CITRINE images achieved approximately 25% reduction in percent diameter stenosis while maintaining consistent contrast levels. These results confirm CITRINE's effectiveness in improving diagnostic accuracy and efficiency in cCTA by leveraging the full potential of multi-energy and PCD-CT technologies.