摘要
目的探讨儿童低剂量副鼻窦CT成像时获得最佳图像质量的自适应性统计迭代重建(ASIR)的最佳权重比例。方法采用宝石能谱CT进行管电压(100 kV和80 kV)和管电流(85 mA和50 mA),随机分4组(100 kV和85 mA,100 kV和50 mA,80 kV和85 mA,80 kV和50 mA),每组各20例进行检查,对原始投影数据分别采用ASIR算法(10%,20%,30%,40%,50%,60%,70%,80%,90%,100%)进行重建,对每例10组图像进行主观评分和客观评价。测量不同重建图像在同一层面的噪声[图像标准差(s)值]及平均CT值,计算对比噪声比(CNR),并进行统计学分析。由2名影像学诊断医师采用双盲法以5分法对图像质量进行主观评价,通过Kappa方法评价观察者间的一致性。结果图像客观噪声值评分随着ASIR权重的提升而降低,CNR值随着ASIR权重的提升而升高,同时图像主观质量评分也有改变,经对照分析图像主观质量最佳权重为40%~60%。结论在进行儿童低剂量副鼻窦CT成像时最佳的ASIR权重比例为40%~60%。
Objective To get the best image quality at low dose in children paranasal sinus computed tomography （CT） imaging adaptive statistical iterative reconstruction （ASIR） the optimal weight ratio. Methods CT performed using gems energy spectrum tube voltage （100 kV and 80 kV） and tube current （85 mA and 50 mA） were randomly divided into four groups （100 kV and 85 mA; 100 kV and 50 mA; 80 kV and 85 mA; 80 kV and 50 mA） were 20 cases in each inspection, the original projection data were used ASIR algorithm （10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%） reconstruction, 10 cases of each group of pictures subjective ratings and objective evaluation. Measurement of different image reconstruction at the same level of noise （standard deviation s of the image） and the average CT value to calculate the contrast to noise ratio （CNR）, and statistical analysis. Imaging diagnosis by two doctors using double-blind method by five points on the image quality subjective evaluations, interobserver consistency through Kappa method. Results Images objective noise value rates with ASIR weight lifting and lowering, contrast to noise ratio （CNR） values with ASIR weight lifting was raised, while subjective image quality scores had changed, the comparative analysis of subjective image quality optimal weight was 40%-60%. Conclusion Carrying children with low-dose CT imaging of the Daranasal sinuses best ASIR weight ratio of 40%-60%.
出处
《实用医技杂志》
2017年第3期245-247,共3页
Journal of Practical Medical Techniques
基金
山东省医药卫生科技发展计划项目(2014WS0308)
关键词
计算机体层摄影
自适应统计迭代重建
儿童
副鼻窦
图像质量
Computed tomography
Adaptive statistical iterative reconstruction
Child
Paranasal sinus
Image quality
