深度学习重建算法在60 kVp超低管电压CT成像中的定量精度与噪声抑制性能:模体研究OA

In this study,we systematically evaluated the iodine quantification accuracy and image noise suppression capabilities of a deep learning reconstruction algorithm(ClearInfinity,CI)under 60 kVp ultra-low tube voltage computed tomography(CT)conditions,comparing it with filtered backprojection(FBP)and hybrid iterative reconstruction(ClearView,CV).A CT performance phantom containing inserts with varying iodine concentrations(40,28,22,12,6,3,and 2 mg/mL)was scanned six times(60 kVp,386 mA)using a NeuViz Epoch Elite CT scanner.Images were reconstructed using FBP,CV(at 20%,40%,60%,and 80%intensities),and CI(at equal intensity).CT values,image noise(standard deviation SD),and coefficients of variation(cv)of the iodine inserts were measured.Absolute percentage bias(APB)and contrast-to-noise ratio(CNR)were calculated.Results show that CI achieved optimal quantitative accuracy at 40%reconstruction intensity and provided the strongest noise reduction at 80%,with a maximum SD reduction of up to 79.59%.At all intensity levels,CI significantly outperformed CV and FBP in terms of APB,noise suppression(especially at low iodine concentrations),measurement stability,and CNR.These findings confirm that CI is an effective solution for producing low-noise,low-bias,and highly stable images in ultra-low-dose CT.