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Development and evaluation of a software tool for the generation of virtual liver lesions in multidetector-row ct datasets

机译：MultiDetector-Row数据集中生成虚拟肝病变的软件工具的开发和评估

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摘要

Rationale and Objectives: Development and evaluation of a software tool for the insertion of simulated hypodense liver lesions in multidetector-row computed tomography (CT) datasets. Materials and Methods: Forty software-generated hypodense liver lesions were inserted at random locations in 20 CT datasets by using the "alpha blending" technique and compared with 40 real metastatic lesions. The location, diameter (5-20 mm) and density of the simulated lesions were individually adjusted to closely resemble real lesions in each patient. Three blinded readers evaluated all 80 lesions twice in a 2-week interval using a five-point Likert confidence scale under standardized conditions. Nonparametric tests were used to statistically evaluate possible differences in scoring between real and simulated lesions. The correctness of the observer rating for real and simulated lesions was compared to chance distribution using the chi-squared statistics. The inter- and intraobserver variability was determined using Kendall's coefficient of concordance. Results: The observer study did not reveal significant differences between the scoring for real versus simulated lesions for any of the readers (P > .05). The distribution of correct and false scoring of the lesions was not significantly different from chance distribution (P > .05). Inter- and intraobserver agreement was poor (Kendall W coefficient = 0.12/0.13). Conclusion: The proposed algorithm is suitable for creating realistic virtual liver lesions in CT datasets. ? 2013 AUR.
机译：理由和目标：在多票行计算断层扫描（CT）数据集中插入模拟脱韵肝病变的软件工具的开发和评估。材料和方法：通过使用“α混合”技术在20ct数据集中的随机位置插入40个软件生成的脱索肝脏病变，并与40个真正的转移性病变进行比较。模拟病变的位置，直径（5-20​​mm）和密度单独调节，以在每位患者中与真实病变紧密相似。在标准化条件下，使用五点李克特信心尺度，三次盲读者在2周间隔两次评估了所有80个病变。非参数测试用于统计上评估真实和模拟病变之间的差异的可能差异。使用CHI方向统计数据与机会分布进行比较了真实和模拟病变的观察者评级的正确性。使用KENDALL的一致性的协调系数确定和陷入内的互联网变异性。结果：观察者研究没有透露对任何读者的真实与模拟病变的评分之间的显着差异（p> .05）。损伤的正确和错误评分的分布与机会分布没有显着差异（P> .05）。和奥里内航空公司协议差（KENDALL W系数= 0.12 / 0.13）。结论：该算法适用于在CT数据集中创建现实虚拟肝病变。还2013年AUR。

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  • 来源
    《Academic radiology》 |2013年第5期|共7页
  • 作者

    KarantzavelosK.; ShinH.-O.; J枚rdensS.; KingB.; RingeK.; HartungD.; WackerF.; VonFalckC.;

  • 作者单位

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Radiology and Nuclear Medicine Klinikum Braunschweig Braunschweig Germany;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

    Department of Diagnostic and Interventional Radiology Hannover Medical School Carl-Neuberg;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    CAD; Computed tomography; Lesion; Liver; Simulation;

    机译：CAD;计算断层扫描;病变;肝脏;模拟;

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