摘要
目的探讨后置基于自适应统计迭代重建(ASiR-V)技术对肺体模图像质量的影响。方法采用GE Revolution CT对肺体模进行扫描,管电压分别采用80、100、120 k V,管电流采用自动m A技术,噪声指数设为12,开启Hi Res扫描模式,采用HD-LUNG模式进行肺重建,以不同权重ASiR-V(0%、10%、20%、30%、40%、50%、60%、70%、80%、90%、100%)重建肺算法图像。记录不同ASiR-V权重下图像背景噪声(SD)作为客观指标,对肺窗显示的支气管血管束(包括肺门区大支气管血管束及胸膜下2 cm范围内较细的支气管血管束)、肺内磨玻璃样病变及肺内结节进行5分制评分法作为主观评分,比较不同k V组间图像质量差异。结果在同一NI时不同k Vp下,随着后置ASiR-V迭代水平增加,噪声水平均显著下降(SD值明显降低),不同权重ASiR-V%下主观评分肺算法ASiR-V在20%~70%的图像质量能较好满足诊断需求(评分≥3分)。结论在同一NI时不同k Vp下,ASiR-V在20%~70%的整体图像质量能较好地满足诊断的需求,但ASiR-V在30%~60%是HD-LUNG模式下观察肺内结构细节的的最佳权重,能完全满足诊断需求。
Objective To explore the impact of ASiR-V at different tube voltages on Volume Hi-definition Detector Revolution CT by evaluating the image quality of a lung phantom in order to find the suitable range of ASiR-V with better image quality.Methods AChest phantom was performed on Revolution CT using 80 kV,100 kV and 120 kV with Hi-res mode,respectively. ASiR-V was pre-set 0% and post-recon( 0%-100%,increment: 10%),auto-mA modulation( 10-500 mA) and the NI was preset as 12.The images were reconstructed using HD lung algrithm. Two radiologists independently evaluated the image quality for the broncho-vascular tracts( including the big broncho-vascular tracts at the pulmonary hilum and the small broncho-vascular tracts ≤2 cm beneath the pleura),pulmonary ground-glass opacity( GGO) and pulmonary nodes at the standard window and lung window to give a subjective score( 1 for poor and 5 for excellent,score 3,impossible to make a diagnosis),SD of background in chest phantom was measured and compared. The image quality was compared statistically among different groups. Results At the same NI,the image noise was gradually reduced as the percentage of ASiR-V increased. 20% - 70% Images of ASiR-V could meet the diagnostic demand. Conclusion At the different tube voltage with the same NI,the reconstructed image using HD lung algorithm combined with percentage of ASiR-V20% - 70% can meet the diagnostic demands. But 30% - 60% ASiR-V is the best weight to observe the details of lung structure under HD-LUNG mode,which can fully meet the diagnostic needs.
作者
杨利莉
刘风娇
高永斌
汪芳
丁婕
赵艳红
YANG Lili;LIU Fengjiao.;GAO YongbinI,;WANG Fang;;DNG Jie.;ZHAO Yanhong..(Ningxia Peopled Hospital Medical imaging Center, Yinchuan 750002, China ;Beifang Univesity of Nationality Teaching Cooperative Hospital, Yinchuan 750002, China)
出处
《宁夏医学杂志》
CAS
2018年第10期868-870,共3页
Ningxia Medical Journal
基金
北方民族大学校级一般科研资助项目(2018YXKY01)