中国医学装备2025，Vol.22Issue(12)：19-23,5.DOI:10.3969/j.issn.1672-8270.2025.12.004
60 kV CT结合深度学习重建算法在儿童胸部超低剂量扫描中的可行性研究
Feasibility study of 60 kV CT combined with deep learning reconstruction algorithm in ultra-low-dose scan for children's chest
摘要
Abstract
Objective:This study aimed to assess the feasibility of 60 kV ultra-low-dose computed tomography(CT)scan combined with deep learning-based reconstruction algorithm for children's chest through a phantom experiment.Methods:This study adopted pediatric chest phantom of PH-1C type.Based on different exposure parameters,the scans were divided into a conventional group(standard dose:100 kV/60 mA)and experimental group[60 kV/with different tube currents:60,30,15,8,5 mA)].The conventional group adopted conventional iterative algorithm(CV algorithm)that weight was 50%to conduct reconstruction,while experimental group adopted respectively 50%CV algorithm and CI algorithm based on deep-learning with different weight(10%,30%,50%,70%,90%)to conduct reconstruction.The differences of subjective and objective images among different algorithms(50%CV,10%CI,30%CI,50%CI,70%CI and 90%CI)within experimental group were compared.The differences of subjective and objective images,and exposure doses were compared between the experimental group[60 kV(60,30,15,8,5 mA)]and the conventional group.Results:Within the experimental group,the reconstructed image quality of CI algorithm was significantly better than that of CV algorithm when the image qualities were compared among different weight reconstructed algorithm,and the differences were significant(F=89.42,5.37,P＜0.05).In them,the objective scores of 50%CI,70%CI,and 90%CI were higher than those of 10%CI and 30%CI,but the differences among the three indicators were not statistically significant(P＞0.05).In subjective evaluation,the image quality of 70%CI was the best.The experimental group[60 kV/different tube current(60,30,15,8,5 mA)]adopted the image that was reconstructed by 70%CI to compare with the image of conventional group,and there were not significant difference at subjective and objective evaluation of image quality between 60 kV/60 mA subgroup of experimental group and conventional group(P＞0.05).There was not significant difference at objective score of lung window between 60 kV/30 mA subgroup and conventional group(P＞0.05),and the subjective score of lung window of 60 kV/30 mA subgroup was slightly less than that of conventional group.The standard deviation(SD)value(12.85±0.65)in objective scores of mediastinal window was higher than that of conventional group,and the signal noise rate(SNR)value(5.07±0.32)was lower than that of conventional group,and the differences were significant(t=14.82,2.26,P＜0.05),while the subjective evaluation was lower than that of conventional group.The parameters of CT radiation dose of all subgroups in the experimental group were significantly lower than those in the conventional group,and the differences were statistically significant(t=215.90,220.01,231.35,237.72,249.70,344.57,368.65,379.50,387.64,392.08,267.48,298.72,301.12,308.07,319.80,P＜0.05).In them,the mean values of effective dose of 60 and 30 mA group of 60 kV were respectively 0.11 and 0.05 mSv.Conclusion:Combined with the 70%CI reconstruction algorithm,the image quality of 60 kV/60 mA can reach or slightly better to the level of conventional 100 kV.For pediatric patients(who receive re-examination for pneumonia)who were especially observed for the images of lung window,and the tube current can be further reduced to 30 mA.关键词
深度学习重建算法/图像质量/体模/儿童超低剂量CTKey words
Deep learning reconstruction algorithm/Image quality/Phantom/Pediatric ultra-low-dose compute tomography(CT)分类
医药卫生引用本文复制引用
黄梦琪,梁傲源,毛苇,周思宏,王冠,宋思思..60 kV CT结合深度学习重建算法在儿童胸部超低剂量扫描中的可行性研究[J].中国医学装备,2025,22(12):19-23,5.基金项目
四川省医学会医学科研项目/青年创新项目(Q2024010) Sichuan Provincial Medical Association Medical Research/Youth Innovation Project(Q2024010) （Q2024010）
