摘要
目的评价肝脏分析软件（1iveranalysisapplication）在肝脏分段及术前评估方面的临床应用价值。方法对30例怀疑肝脏疾病的患者进行腹部肝脏增强CT检查。患者分为肝内占位性病变组（实验组B）和无肝内占位组（对照组A），每组各15例。利用肝脏分析软件对所有患者的门脉期原始数据进行重建分析。测量肝脏及肝内病变体积，并根据血管走行及分布对肝脏进行自动分段。使用SPSS15．0软件、t检验、x^2检验和非参数Mann-WhitneyU法分析检测数据。结果软件后处理过程两组差异无统计学意义（P=0．361）；实验组肝脏体积为（1374．61±444．05）cm^3，对照组肝脏体积为（1225．70±272．07）cm^3（P=0．108）。血管识别准确率实验组I级为7例（46．7％），Ⅱ级为5例（33．3％），Ⅲ级为3例（20％）；对照组I级为7例（46．7％），11级为6例（40％），Ⅲ级为2例（13．3％）（P=0．87），两组血管识别率差异无统计学意义。两组共有21段血管识别错误，其中18段为肝静脉，占85．71％（18／21）。结论64排螺旋CT肝脏分析软件可以直观显示肝脏病变、血管及肝段解剖，并进行定量分析，对肝脏病变术前评估、手术方案制定有很大意义。
Objective To determine the value of liver analysis application in liver segmentation and planning of surgery. Methods Thirty patients suspected having hepatic disease were recruited in this study. Contrast-enhanced CT examinations were performed with Philips Brilliance 64-slice CT, and multi-phase images were obtained. The patients were divided into group B （with focal hepatic lesion, 15 patients）, and group A （without hepatic lesion, 15 patients）. We use the portal-venous （60- 70 s） images to analysis. Liver volume and vessel recognition were edited manually if necessary, then liver segmentation proceeded automatically. All data were analyzed by the t test, chi-square test, Mann-Whitney U analysis, with SPSS 15.0 software. Results There was no significant difference of post-processing procedure between the two groups （P= 0. 361）. The liver volume was （1374.61 ±444.05） cm^3 in the group B and （1225.70±272.07） cm^3 in the group A （P=0. 108）. The accuracy of vessel recognition was no significant difference between the two groups （P=0. 87）. 21 vessels were recognized incorrectly include 18 hepatic veins. Conclusion The liver analysis application provides a 3D reconstruction allow vivid observation of liver segmentation and accurate estimation of the liver volume. It has broad prospect in diagnosing and surgical planning of the liver disease.
出处
《中华肝胆外科杂志》
CAS
CSCD
北大核心
2012年第3期192-195,共4页
Chinese Journal of Hepatobiliary Surgery
关键词
计算机体层摄影
肝脏
图像处理
肝段
Computed tomography
Liver
Image processing
Liver segmentation
