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脑内血肿体积的CT测量研究 被引量:4

Study of CT measurement of volume of intracerebral hematoma
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摘要 目的:探讨脑内血肿体积(V)值与轴位CT图像上长(A)×宽(B)×高(C)值的相关性,并求两者的线性回归方程。方法:搜集83例脑内血肿患者的临床及影像资料,将层厚0.625 mm薄层图像传送ADW 4.5工作站,行VR并测量血肿体积值,在PACS上测量层厚5 mm图像的血肿A、B、C值,计算A×B×C值;通过SPSS 21.0统计学软件绘制出两者的散点图,算出相关系数r;通过线性回归方程Y=a+bX得出常数项a和回归系数b值。结果:V值与A×B×C值的散点图呈线性相关,两者存在正相关关系(r=0.991,P<0.001)。通过线性回归方程Y=a+bX得出a=0.737,b=0.432,血肿体积V=0.737+0.432×(A×B×C)。结论:回归方程脑内血肿体积V=0.737+0.432×(A×B×C),具有一定的准确性,可为血肿的测量提供依据。 Objective:To investigate the correlation between volume of intracerebral hematoma(V value)and CT hematoma length(A)×width(B)×height(C)value of axis,and to find out the linear regression equation of both.Methods:83 cases of intracerebral hematoma were collected,ADW 4.5 workstation was transmitted by thin layer 0.625 mm image,hematoma was reconstructed by VR and V value was measured.The A,B,C values of the 5 mm image were measured on PACS.The values of A×B×C were calculated,the scattered point diagrams of the A×B×C and V value were drawn through SPSS 21.0 statistical software,the correlation coefficient r was calculated,and the constant number a and regression coefficient b values were obtained by linear regression equation Y=a+bX.Results:The scattered maps drawn by SPSS 21.0 statistical software were linearly correlated,and the two values had a positive correlation(r=0.991,P<0.001).The constant term a=0.737,the regression coefficient b=0.432,and the intracerebral hematoma volume V=0.737+0.432×(A×B×C)were obtained by the linear regression equation Y=a+bX.Conclusion:The regression equation of hematoma volume V=0.737+0.432×(A×B×C)has a certain accuracy,which provides a new formula for reference for hematoma measurement.
作者 梁树生 丘文科 陈华英 莫永灿 黄大成 LIANG Shusheng;QIU Wenke;CHEN Huaying;MO Yongcan;HUANG Dacheng(Imaging Center,Yingde People’s Hospital,Yingde 513000,China)
出处 《中国中西医结合影像学杂志》 2020年第3期254-256,共3页 Chinese Imaging Journal of Integrated Traditional and Western Medicine
关键词 脑内血肿 体积 线性相关 体层摄影术 X线计算机 Intracerebral hematoma Volume Linear correlation Tomography X-ray computed
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