Leng, Shuai

Abstract Background Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy‐integrating detectors (EIDs). Purpose To develop a convolutional neural network ( I mproved LUMEN visualization through A rtificial super‐resolu T ion imag E s (ILUMENATE)) informed by photon‐counting‐detector (PCD)‐CT to improve EID‐CT image resolution and determine its impact on cCTA. Materials and methods With IRB approval, 30 patients undergoing clinically indicated cCTA were scanned with EID‐CT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and subsequently with ultra‐high‐resolution (UHR) PCD‐CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. ILUMENATE was trained on eight patient PCD‐CT datasets (67,890 patch pairs with 90% for training (61,101), 10% reserved for validation (6,789)) and applied to 22 unseen EID‐CT cases. Spatial resolution was evaluated using line profiles and percent diameter stenosis quantified with a severity score assigned. Two experienced radiologists, blinded to image type, selected preferred series and scored images for overall quality, sharpness, and noise comparing original EID‐CT and ILUMENATE output. Results Visual assessment and line profiles showed substantial resolution improvement with ILUMENATE. Percent diameter stenosis was significantly reduced (mean ± standard deviation: 4.42% ± 4.82%) using ILUMENATE ( p < 0.001) with nine lesions shifting down in severity score. Readers preferred ILUMENATE images in 22/22 cases and scored ILUMENATE superiorly for overall quality, sharpness, and noise ( p < 0.05). Conclusions ILUMENATE enhanced image resolution, resulting in improved overall image quality, reduced calcium blooming artifacts, and improved lumen visibility in cCTA exams performed using EID‐CT. This could potentially allow for improved accessibility to UHR image quality, allowing for more accurate assessment of CAD.