[1]邓靓娜,张斌,蒋健,等.窗宽和层厚在COVID-19磨玻璃征象中的诊断价值[J].中国医学物理学杂志,2020,37(5):619-624.[doi:10.3969/j.issn.1005-202X.2020.05.017]
　DENG Liangna,,et al.Diagnostic value of window width and slice thickness in ground glass opacity of COVID-19[J].Chinese Journal of Medical Physics,2020,37(5):619-624.[doi:10.3969/j.issn.1005-202X.2020.05.017]
点击复制

窗宽和层厚在COVID-19磨玻璃征象中的诊断价值()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第5期
页码:
619-624
栏目:
医学影像物理
出版日期:
2020-05-25

文章信息/Info

Title:
Diagnostic value of window width and slice thickness in ground glass opacity of COVID-19
文章编号:
1005-202X（2020）05-0619-06
作者:
邓靓娜1; 2; 3; 张斌1; 2; 3; 蒋健1; 2; 3; 林晓强1; 2; 3; 韩涛1; 2; 3; 景梦园1; 2; 3; 周俊林1; 2; 3
1. 兰州大学第二医院放射科，甘肃兰州730030；2. 兰州大学第二临床医学院，甘肃兰州730000；3. 甘肃省医学影像重点实验室， 甘肃兰州730030
Author(s):
DENG Liangna1;  2;  3;  ZHANG Bin1;  2;  3;  JIANG Jian1;  2;  3;  LIN Xiaoqiang1;  2;  3;  HAN Tao1;  2;  3;  JING Mengyuan1;  2;  3;  ZHOU Junlin1;  2;  3
1. Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China 2. Second School of Clinical Medicine, Lanzhou University, Lanzhou 730000, China 3. Key Laboratory of Medical Imaging in Gansu Province, Lanzhou 730030, China
关键词:
COVID-19; 窗宽; 层厚; 磨玻璃影; 高分辨率CT
Keywords:
COVID-19 window width slice thickness ground glass opacity high-resolution computed tomography
分类号:
R445.3；R814.42
DOI:
10.3969/j.issn.1005-202X.2020.05.017
文献标志码:
A
摘要:
目的：探讨并筛选适合观察新型冠状病毒肺炎（COVID-19）中磨玻璃影及各征象的最佳窗宽及层厚设置方案。方法： 回顾性分析11例COVID-19患者的影像资料，分别选取30个磨玻璃影、铺路石征、血管影增粗及空气支气管征的COVID-19 病灶进行分析对照，两名具有15年以上诊断经验的胸部影像医生对固定窗位下不同窗宽及层厚下的磨玻璃影及其他各征 象的显示程度进行主观评分，得出图像质量评分。结果：对磨玻璃影显示为3分的占比最大的窗宽值为1 000 HU（76.7%）， 对铺路石征显示为3分的占比最大的窗宽值为1 400 HU（80.0%），对血管影增粗显示为3分的占比最大的窗宽值为1 400 HU （83.3%），对空气支气管征显示为3分的占比最大的窗宽值为1 000 HU（83.3%），以上结果与其他窗宽组的显示结果比较均 具有显著统计学意义（P<0.001）。对磨玻璃影、铺路石征、血管影增粗及空气支气管征显示为3分的占比最多的层厚均为1 mm（100%）；与其他层厚组比较，差异均具有显著统计学意义（P<0.001）。结论：在COVID-19的影像诊断过程中，1 000 HU 是观察磨玻璃影及空气支气管征的最佳窗宽值，1 400 HU是观察铺路石征及血管影增粗的最佳窗宽值；1 mm是观察磨玻璃 影及各征象的最佳层厚值。
Abstract:
Objective To explore and select the best setting scheme of window width and slice thickness which is suitable for observing the ground glass opacity and various signs of corona virus disease 2019（COVID-19）. Methods The imaging data of 11 patients with COVID-19 were analyzed retrospectively and 30 COVID-19 lesions with ground glass opacity, paving stone sign, vascular thickening and air bronchogram sign were selected for analysis and comparison. The display degrees of ground glass opacity and the other signs under different window widths and slice thicknesses at the fixed window level were subjectively scored by two chest imaging doctors with more than 15 years of diagnostic experience for obtaining the image quality score. Results The window widths with the highest percentage for the ground glass opacity with a score of 3, the paving stone sign with a score of 3, vascular thickening with a score of 3 and air bronchogram sign with a score of 3 were 1 000 HU (76.7%), 1 400 HU (80.0%), 1 400 HU (83.3%) and 1 000 HU (83.3%), respectively, and there were statistical differences between the above results and those in other groups (P<0.001). For the signs (ground glass opacity, paving stone sign and vascular thickening and air bronchogram sign) with the highest score, the slice thickness with the highest proportion was 1 mm (100%), and the results were significantly different from those in other groups (P<0.001). Conclusion During the imaging diagnosis of COVID-19, the best window width for observing ground glass opacity and air bronchogram sign is 1 000 HU, and that for observing paving stone sign and vascular thickening is 1 400 HU.Moreover, 1 mm is the best slice thickness for observing ground glass opacity and various signs.

相似文献/References:

[1]刘发明,江桂华,杨宁,等.新型冠状病毒肺炎的影像组学研究[J].中国医学物理学杂志,2020,37(4):463.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.012]
　LIU Faming,JIANG Guihua,YANG Ning,et al.Radiomics analysis on COVID-19[J].Chinese Journal of Medical Physics,2020,37(5):463.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.012]
[2]乔文俊,许乙凯,严承功,等.新型冠状病毒肺炎期间方舱CT感染防控经验[J].中国医学物理学杂志,2020,37(7):903.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.020]
　QIAO Wenjun,XU Yikai,et al.Experience of COVID-19 prevention and control in shelter CT[J].Chinese Journal of Medical Physics,2020,37(5):903.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.020]
[3]李碧草,王晶,郭旭伟,等.基于多尺度特征融合与反向注意力的COVID-19病灶分割[J].中国医学物理学杂志,2023,40(4):403.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.002]
　LI Bicao,WANG Jing,GUO Xuwei,et al.COVID-19 lesion segmentation based on multi-scale feature fusion and reverse attention[J].Chinese Journal of Medical Physics,2023,40(5):403.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.002]
[4]张子宇,赵可辉,牛慧芳,等.基于轻量级RG-DenseNet的COVID-19 CT图像分类[J].中国医学物理学杂志,2023,40(12):1494.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.007]
　ZHANG Ziyu,ZHAO Kehui,NIU Huifang,et al.COVID-19 classification on CT image using lightweight RG DenseNet[J].Chinese Journal of Medical Physics,2023,40(5):1494.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.007]
[5]齐瑜鹏,赵言龙,郑浩然.一种通过代谢差异分析抑制SARS-CoV-2复制的靶点预测方法[J].中国医学物理学杂志,2023,40(12):1577.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.019]
　QI Yupeng,ZHAO Yanlong,ZHENG Haoran.Target prediction approach to inhibit SARS-CoV-2 replication based on metabolic difference analysis[J].Chinese Journal of Medical Physics,2023,40(5):1577.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.019]

备注/Memo

备注/Memo:
【收稿日期】2020-04-07 【基金项目】兰州市人才创业创新项目（2016-RC-58） 【作者简介】邓靓娜，硕士研究生，研究方向：神经影像，E-mail: 23128- 44186@qq.com 【通信作者】周俊林，博士，主任医师，教授，研究方向：神经影像，E-mail: lzuzjl601@163.com
更新日期/Last Update: 2020-06-03