首页> 外文期刊>Magnetic resonance imaging: An International journal of basic research and clinical applications >Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways
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Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways

机译:人类的微观结构知识的慢散扩程牵引牵引牵引性增强了纤维途径的可视化

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AbstractConventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). Highb-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of “microstructure-informed” whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm?2at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-expo
机译:<![cdata [ 抽象 基于扩散张量成像的传统光纤牵引方法利用扩散各向异性和方向性在低扩散权的范围内(< CE:斜体> B - 值)。 HIGH B -Value biexponential扩散张量分析先前已经证明,慢延伸部件的分数各向异性基本上高于传统扩散张量成像的,而流行隔间模型将该慢速扩散组件相关联随着轴突水分。本研究的主要目的之一是阐明“微观结构知识”全脑慢 - 扩散纤维跟踪(SDIFT)的可行性和潜在益处。在延伸范围的扩散权重范围≤6000smmΔ2在3t的范围内获得了人类中的扩散加权图像。使用双指数张量分解重建快速和慢速扩散张量,并进行了相关的全脑张量度量的详细统计分析。我们使用慢散射张量的主要特征媒体,在体内脑大脑中显示三维纤维束。特别地,我们证明了慢扩散纤维跟踪提供了长结合纤维的相当高的纤维计数,并且允许比传统扩散张量成像重建更多的短关联纤维。建议称为SDIFT作为一种互补方法,可以增强评估的光纤途径的可靠性和可视化。在Mono-expo的不确定性的情况下,尤其是尤其是卓越的地区

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