Bogovic John A, Carass Aaron, Wan Jing, Landman Bennett A, Prince Jerry L
Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University.
Proc IEEE Int Symp Biomed Imaging. 2008 May:895. doi: 10.1109/ISBI.2008.4541141.
Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health of white matter in the brain. Tractography, the tracing of individual fibers in the brain using DTI data, has begun to play a more central role in neuroscience research, particularly in understanding the relationships between brain connectivity and behavior. The measuring of features related to bundles of fibers, i.e., tracts or fasciculi, is currently problematic because of the need for manual interaction. This article presents an algorithm for the automatic identification of selected white matter tracts. It extracts fibers using the FACT algorithm and finds cortical gyral labels using a multi-atlas deformable registration scheme. Tracts are identified as the fibers passing between selected cortical labels. The quality of automatic labels are compared both visually and numerically against a well-accepted manual approach. The automatic approach is shown to be more consistent with conventional definitions of tracts and more repeatable on separate scans of the same subject.
扩散张量成像（DTI）已成为评估大脑白质健康状况的标准临床程序。纤维束成像，即利用DTI数据追踪大脑中的单个纤维，已开始在神经科学研究中发挥更核心的作用，尤其是在理解大脑连通性与行为之间的关系方面。由于需要人工交互，目前测量与纤维束（即束或纤维束）相关的特征存在问题。本文提出了一种自动识别选定白质束的算法。它使用FACT算法提取纤维，并使用多图谱可变形配准方案找到皮质脑回标签。束被识别为在选定皮质标签之间通过的纤维。将自动标签的质量与一种广泛接受的手动方法进行视觉和数值比较。结果表明，自动方法与束的传统定义更一致，并且在同一受试者的单独扫描中更具可重复性。
