Effects of rejecting diffusion directions on tensor-derived parameters.
Effects of rejecting diffusion directions on tensor-derived parameters.
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DOI：
10.1016/j.neuroimage.2015.01.010
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发表时间：
2015-04-01
期刊：
影响因子：
5.7
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Chen Y;Tymofiyeva O;Hess CP;Xu D
扩散张量成像(DTI)受到被摄体运动的不利影响。在进行扩散参数估计之前，有必要将受污染的图像去掉。然而，拒绝这些图像的后果并没有得到很好的理解。在这项研究中，我们通过分析分数各向异性(FA)、平均扩散系数(MD)、轴向扩散系数(AD)、径向扩散系数(RD)和主特征向量(V1)的变化来研究排除一个或多个体积的扩散加权图像的效果。基于Jones 30扩散方案获得的全集扩散图，我们通过随机、均匀和聚类拒绝三种不同的方法生成至少六个不完整集。结果表明，拒绝扩散方向对MD没有显著影响。在随机拒绝的情况下，随着拒绝次数的增加，FA、AD、RD和V1被高估的程度更大，且低FA区的高估程度比高FA区更严重。对于均匀拒绝，即剩余扩散方向在球面上均匀分布，在FA和V1中几乎没有观察到变化。另一方面，簇状拒绝表现出对参数的最显著高估，所产生的准确性取决于底层纤维相对于排除方向的相对取向。在实践中，如果排除扩散方向数据，重要的是要注意被拒绝的方向的数量和位置，以便对数据进行更准确的分析。
Diffusion Tensor Imaging (DTI) is adversely affected by subject motion. It is necessary to discard the corrupted images before diffusion parameter estimation. However, the consequences of rejecting those images are not well understood. In this study, we investigated the effects of excluding one or more volumes of diffusion weighted images by analyzing the changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and the primary eigenvector (V1). Based on the full set of diffusion images acquired by Jones30 diffusion scheme, we generated incomplete sets of at least six in three different ways: random, uniform and clustered rejections. The results showed that MD was not significantly affected by rejecting diffusion directions. In the cases of random rejections, FA, AD, RD and V1 were overestimated more greatly with increasing number of rejections and the overestimations were worse in low FA regions than high FA regions. For uniform rejections, at which the remaining diffusion directions are evenly distributed on a sphere, little change was observed in FA and in V1. Clustered rejections, on the other hand, displayed the most significant overestimation of the parameters, and the resulting accuracy depended on the relative orientation of the underlying fibers with respect to the excluded directions. In practice, if diffusion direction data is excluded, it is important to note the number and location of directions rejected, in order to make a more precise analysis of the data.
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