Factors affecting the voxel-based analysis of diffusion tensor imaging
Factors affecting the voxel-based analysis of diffusion tensor imaging
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DOI：
10.1007/s11434-014-0551-8
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发表时间：
2014-08
影响因子：
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通讯作者：
Jianli Wang;B. Nie;Haitao Zhu;Hua Liu;Jingjuan Wang;S. Duan;B. Shan
中科院分区：
文献类型：
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作者：
Jianli Wang;B. Nie;Haitao Zhu;Hua Liu;Jingjuan Wang;S. Duan;B. Shan
扩散张量成像（DTI）提供了一种独特的方法来显示完整的白色物质的微观结构无创。基于体素的分析（Voxel-based analysis，简称VXI）是一种可重复性高、不依赖于用户的技术，已在许多研究中被用于分析DTI数据。分数各向异性（FA），这是来自DTI，是最常用的参数。DTI数据预处理过程中的参数设置可能会影响FA分析结果。然而，没有可靠的证据表明参数如何影响FA分析的结果。本研究旨在定量研究可能影响基于体素的FA分析的因素，这些因素包括空间归一化过程中的插值，平滑核和统计阈值。由于很难获得患者病变的真实信息，我们在健康FA图上模拟病变。20例健康受试者的DTI数据。使用DTISutio计算FA图。我们将这些FA图随机分为两组。一组为模型组，另一组为正常对照组。通过将指定区域的FA强度降低5%-50%，将模拟病变添加到模型患者组中。模型组和正常对照组采用双样本t检验逐体素统计分析检测模拟病灶。我们通过比较通过MRI检测到的病变与模拟病变之间的差异来评估这些因素。结果表明，FA图像的空间归一化应采用三线性插值，平滑核应为空间归一化FA图像体素大小的2-3倍。对于强度变化较小的病变，必须谨慎使用FWE校正。本研究为用荧光法分析脂肪酸提供了重要参考。
Diffusion tensor imaging (DTI) provides a unique method to reveal the integrity of white matter microstructure noninvasively. Voxel-based analysis (VBA), which is a highly reproducible and user-independent technique, has been used to analyze DTI data in a number of studies. Fractional anisotropy (FA), which is derived from DTI, is the most frequently used parameter. The parameter setting during the DTI data preprocessing might affect the FA analysis results. However, there is no reliable evidence on how the parameters affect the results of FA analysis. This study sought to quantitatively investigate the factors that might affect the voxel-based analysis of FA; these include the interpolation during spatial normalization, smoothing kernel and statistical threshold. Because it is difficult to obtain the true information of the lesion in the patients, we simulated lesions on the healthy FA maps. The DTI data were obtained from 20 healthy subjects. The FA maps were calculated using DTIStudio. We randomly divided these FA maps into two groups. One was used as a model patient group, and the other was used as a normal control group. Simulated lesions were added to the model patient group by decreasing the FA intensities in a specified region by 5 %–50 %. The model patient group and the normal control group were compared by two-samplettest statistic analysis voxel-by-voxel to detect the simulated lesions. We evaluated these factors by comparing the difference between the detected lesion through VBA and the simulated lesion. The result showed that the space normalization of FA image should use the trilinear interpolation, and the smoothing kernel should be 2–3 times the voxel size of spatially normalized FA image. For lesions with small intensity change, FWE correction must be cautiously used. This study provided an important reference to the analysis of FA with VBA method.