深度学习重建算法联合超高分辨力探测器对眼眶CT图像质量的影响OA

Objective:This study investigates the effect of a 0.312 5 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×0.312 5 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 128×0.312 5 mm showed significantly better performances in terms of MTF50%,MTF10%,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 128×0.312 5 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 0.312 5 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.