The Impact of Deep Learning Reconstruction Algorithm Combined with Ultra-high Resolution Detector on Orbital CT Image Quality

Objective: This study investigates the effect of a 0.3125 mm ultra-high-resolution detector combined with a ClearInfinity (CI) deep-learning reconstruction algorithm on the image quality of orbital computed tomography (CT). Methods: Scans were performed using a NeuViz Epoch Elite CT scanner on a Catphan 600 phantom and three 7-year-old rhesus monkeys. The collimation widths were set to 64 mm×0.625 mm and 128 mm×0.3125 mm. Images were acquired using filtered back projection (FBP), 60% adaptive iterative reconstruction algorithm ClearView (CV), and 60% deep learning reconstruction algorithm CI. Image quality was evaluated using objective indicators such as the modulation transfer function (MTF) and contrast-to-noise ratio (CNR), as well as using double-blind subjective scoring. Additionally, statistical analyses were performed. Results: In phantom experiments, under standard and bone algorithms, images with a collimation width of 128×0.3125 mm showed significantly better performances in terms of MTF_50%, MTF_10%, and some CNR indicators compared with those with a collimation width of 64×0.625 mm. The CNR of the CI algorithm was significantly higher than those of the FBP and CV algorithms. In animal experiments, the CNR of the medial rectus in images with a 128×0.3125 mm collimation width was significantly higher than that in images with a 64×0.625 mm collimation width. The CI algorithm achieved the optimal CNR for the medial rectus and eyeball, as well as the highest subjective scores, with good consistency between two radiologists’ subjective scores (Kappa≥0.75). Conclusion: The 0.3125 mm ultra-high-resolution detector combined with the CI deep-learning algorithm significantly improved the resolution and contrast of orbital CT images as well as reduced noise and artifacts, thereby demonstrating promising clinical-application prospects.