从弥散牵引成像中通常得出的定量指标与流线长度相关:特征描述和调整方法

IF 2.7 3区 医学 Q1 ANATOMY & MORPHOLOGY
Richard G. Carson, Alexander Leemans
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引用次数: 0

摘要

通过将弥散张量成像(DTI)或其他模型应用于弥散加权(DW)磁共振成像(MRI)数据所产生的体素信息,束流成像算法被广泛用于划分白质结构。通过统计建模,我们证明了这些方法通常会在流线长度与分数各向异性(FA)等几种束学衍生定量指标之间产生实质性的系统关联。这些关联可被描述为片断线性关系。对于短于拐点的流线(根据为每个大脑划定的一组束确定),FA 估计值与流线长度呈正线性关系。对于长于拐点的流线,两者之间的关系较弱,流线长度与 FA 之间的斜率与零仅有微小差别。由于这种关联在人脑 DW 成像中常见的流线长度范围(小于约 100 毫米)内最为明显,我们的研究结果表明,如果不考虑流线长度的变化,从扩散束成像中得出的一些定量指标有可能产生误导。本文介绍了一种方法,可利用线性、布莱克曼和片断线性模型预测的阿凯克信息加权平均值,在每个标本的整个流线长度范围内,有效补偿 FA(和其他定量指标)与流线长度的关联。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Quantitative metrics commonly derived from diffusion tractography covary with streamline length: a characterization and method of adjustment

Quantitative metrics commonly derived from diffusion tractography covary with streamline length: a characterization and method of adjustment

Tractography algorithms are used extensively to delineate white matter structures, by operating on the voxel-wise information generated through the application of diffusion tensor imaging (DTI) or other models to diffusion weighted (DW) magnetic resonance imaging (MRI) data. Through statistical modelling, we demonstrate that these methods commonly yield substantial and systematic associations between streamline length and several tractography derived quantitative metrics, such as fractional anisotropy (FA). These associations may be described as piecewise linear. For streamlines shorter than an inflection point (determined for a group of tracts delineated for each individual brain), estimates of FA exhibit a positive linear relation with streamline length. For streamlines longer than the point of inflection, the association is weaker, with the slope of the relationship between streamline length and FA differing only marginally from zero. As the association is most pronounced for a range of streamline lengths encountered typically in DW imaging of the human brain (less than ~ 100 mm), our results suggest that some quantitative metrics derived from diffusion tractography have the potential to mislead, if variations in streamline length are not considered. A method is described, whereby an Akaike information weighted average of linear, Blackman and piecewise linear model predictions, may be used to compensate effectively for the association of FA (and other quantitative metrics) with streamline length, across the entire range of streamline lengths present in each specimen.

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来源期刊
Brain Structure & Function
Brain Structure & Function 医学-解剖学与形态学
CiteScore
6.00
自引率
6.50%
发文量
168
审稿时长
8 months
期刊介绍: Brain Structure & Function publishes research that provides insight into brain structure−function relationships. Studies published here integrate data spanning from molecular, cellular, developmental, and systems architecture to the neuroanatomy of behavior and cognitive functions. Manuscripts with focus on the spinal cord or the peripheral nervous system are not accepted for publication. Manuscripts with focus on diseases, animal models of diseases, or disease-related mechanisms are only considered for publication, if the findings provide novel insight into the organization and mechanisms of normal brain structure and function.
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