Neurospin, CEA, Gif-sur-Yvette, France.
Neuroimage. 2011 Feb 1;54(3):1975-93. doi: 10.1016/j.neuroimage.2010.10.028. Epub 2010 Oct 18.
This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles.
本文提出了一种聚类方法，用于检测嵌入任何基于磁共振扩散的束流追踪数据集的纤维束。我们的方法可以看作是一种压缩操作，捕获纤维数据集中包含的最有意义的信息。为了提高效率，分析的一部分是基于对大脑白质（WM）体素进行聚类，而不是对纤维进行聚类。由此产生的感兴趣区域被用于定义使用纤维末端聚类进一步细分为一致束的纤维子集。数据集从超过一百万条纤维束减少到大约两千条纤维束。使用模拟数据和物理体模进行了验证。我们认为我们的方法是在对大型纤维数据集进行进一步分析之前的一个关键预处理步骤。一个重要的应用将是推断大脑白质通路细分的详细模型以及 U 型主要纤维束的映射。
