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基于骨架的扩散张量磁共振成像数据分析中统计灵敏度的空间和方向异质性。

Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor MR imaging data.

机构信息

Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

出版信息

J Neurosci Methods. 2011 Sep 30;201(1):213-9. doi: 10.1016/j.jneumeth.2011.07.025. Epub 2011 Jul 30.

DOI:10.1016/j.jneumeth.2011.07.025
PMID:21835201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3188429/
Abstract

Group comparisons of indices derived from diffusion tensor imaging are common in the literature. An increasingly popular approach to performing such comparisons is the skeleton-projection based approach where, for example, fractional anisotropy (FA) values are projected onto a skeletonized version of the data to minimize differences due to spatial misalignment. In this work, we examine the spatial heterogeneity of the statistical power to detect group differences, and show that there is an intrinsic spatial heterogeneity, with more 'central' structures having less variance within a population. Importantly, we also demonstrate a previously unreported feature of skeleton-based analysis methods, that is that the width of the skeleton depends on the relative orientation to the imaging matrix. Due to the way in which the inferential statistics are performed, this means that structures that are obliquely oriented to the imaging matrix are more likely to show significant differences than when aligned with the imaging matrix. This has profound implications for the interpretation of results obtained from such analysis, especially when there are no a priori hypotheses concerning the spatial location of any group differences. For a uniform (DC) offset between two groups, the skeleton projection-based approaches will be most likely to reveal a difference in centrally located white matter structures oriented obliquely to the imaging matrix.

摘要

在文献中,对来自扩散张量成像的指数进行组间比较是很常见的。目前,一种越来越流行的执行此类比较的方法是基于骨架投影的方法,例如,将各向异性分数(FA)值投影到数据的骨架化版本上,以最大程度地减少由于空间错位引起的差异。在这项工作中,我们研究了检测组间差异的统计功效的空间异质性,并表明存在内在的空间异质性,更多的“中心”结构在群体内的方差更小。重要的是,我们还展示了基于骨架分析方法的一个以前未报道的特征,即骨架的宽度取决于相对于成像矩阵的相对方向。由于推断统计的执行方式,这意味着相对于成像矩阵倾斜的结构比与成像矩阵对齐的结构更有可能显示出显著差异。这对从这种分析中获得的结果的解释具有深远的影响,特别是当没有关于任何组间差异的空间位置的先验假设时。对于两组之间的均匀(DC)偏移,基于骨架投影的方法最有可能揭示出与成像矩阵倾斜的中央白质结构的差异。

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