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Tract Probability Maps in Stereotaxic Spaces: Analyses of White Matter Anatomy and Tract-Specific Quantification

机译:立体定向空间中的牵引概率图:白色物质解剖学和特定于牵引的量化分析

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摘要

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.
机译:扩散张量成像(DTI)是一种令人兴奋的新MRI方式,可以揭示白质的详细解剖结构。 DTI还使我们能够近似主要白质束的3D轨迹。通过将识别出的区域坐标与各种类型的MR参数图(例如T2和扩散属性)结合起来,我们可以对这些参数进行区域特定的分析。不幸的是,噪声,局部体积效应和复杂的轴突结构损害了3D道重建。此外,病理条件下扩散各向异性的变化可能会改变3D束重建的结果。在这项研究中,我们基于从28个正常受试者的DTI数据中得出的11个主要白质束的概率图,创建了一个白质分离图集。使用这些概率图,对分数各向异性和平均扩散率进行了特定于区域的自动化定量。在自动的和基于个人的影像学检查结果之间发现极好的相关性。该工具可以对多个白质区的状态进行有效的初始筛查。

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