Learned high resolution energy-integrating detector CT angiography: Harnessing the power of ultra-high-resolution photon counting detector CT.

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 (Improved LUMEN visualization through Artificial super-resoluTion imagEs (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.