纤维束成像
体素
磁共振弥散成像
计算机科学
反褶积
流线、条纹线和路径线
概率逻辑
人工智能
白质
透视图(图形)
纤维
集合(抽象数据类型)
方向(向量空间)
算法
计算机视觉
磁共振成像
数学
物理
几何学
医学
放射科
程序设计语言
热力学
化学
有机化学
作者
Jacques‐Donald Tournier,Fernando Calamante,Alan Connelly
摘要
Abstract In recent years, diffusion‐weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 53–66, 2012
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