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尘肺观察对象的CT定量分析 被引量:11

Compute tomography-based quantitative evaluation of pneumoconiosis
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摘要 目的探讨CT定量分析对尘肺的诊断价值。方法对尘肺观察对象组(60例)及正常对照组(40例)进行CT扫描,分别在主动脉弓顶、气管隆突、气管隆突下3 cm、气管隆突下6 cm平面进行CT定量测定,用CT密度直方图对特定的CT值区间进行测定,计算出不同CT值范围内的平均肺密度(ME)及像素指数(PI),对比观察尘肺观察对象组肺密度的改变。结果在主动脉弓顶水平、气管隆突下6 cm层面,-832--352 HU范围的10个CT值区间内,尘肺观察对象组像素指数均高于正常对照组;在气管隆突、气管隆突下3 cm层面,-880--352 HU范围的11个CT值区间内,尘肺观察对象组像素指数均高于正常对照组的像素指数。其共同特征为,上述4个观察层面,在-832--352 HU范围内的10个CT值区间内,尘肺观察对象组的像素指数均高于正常对照组。尘肺观察对象组与正常对照组间差异有统计学意义(P<0.05)。结论 CT定量直方图信息,能够评价尘肺观察对象肺纤维化改变,结果可靠,客观,为尘肺观察对象的诊断提供了一种新的定量诊断方法。 Objective To study the value of compute tomography (CT)-based quantitative assessment in the diagnosis of pneumoconiosis. Methods Sixty patients with pneumoconiosis and 40 healthy volunteers (control) underwent CT scanning at the levels of the top of the aortic arch, tracheal carina, and 3 cm and 6 cm below the tracheal carina. All the CT images were analyzed with density histograms for a specific region to calculate the mean lung CT value (ME) and pixel index for assessment of lung density changes. Results At the levels of the top of the aortic arch and 6 cm below the tracheal carina, the pixel indices in the 10 CT threshold density intervals within-832 to-352 HU was all significantly higher in pneumoconiosis group than in the control group (P〈0.05). At the levels of the tracheal carina and 3 cm below the tracheal carina, the 11 pixel indices in the CT threshold density intervals within -880 to -352 HU were also significantly higher in pneumoconiosis group (P〈 0.05). At all the 4 scan levels, the pixel indices in the 10 intervals between-880 and-352 HU were all significantly higher in pneumoconiosis group (P〈0.05). Conclusion CT density histograms allow quantitative evaluation of lung fibrosis in patients with pneumoconiosis for diagnostic purposes.
出处 《南方医科大学学报》 CAS CSCD 北大核心 2012年第12期1768-1772,共5页 Journal of Southern Medical University
基金 重庆市卫生局医学科学技术重点项目(2009-1-108)
关键词 尘肺观察对象 CT扫描 定量分析 像素直方图 像素指数 pneumoconiosis computed tomography quantitative analysis density histogram pixel index.
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