Guevara Pamela, Poupon Cyril, Rivière Denis, Cointepas Yann, Marrakchi Linda, Descoteaux Maxime, Fillard Pierre, Thirion Bertrand, Mangin Jean-François
Neurospin, CEA, Gif-sur-Yvette, France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):550-7. doi: 10.1007/978-3-642-15705-9_67.
This paper presents a method inferring a model of the brain white matter organisation from HARDI tractography results computed for a group of subjects. This model is made up of a set of generic fiber bundles that can be detected in most of the population. Our approach is based on a two-level clustering strategy. The first level is a multiresolution intra-subject clustering of the million tracts that are computed for each brain. This analysis reduces the complexity of the data to a few thousands fiber bundles for each subject. The second level is an intersubject clustering over fiber bundle centroids from all the subjects using a pairwise distance computed after spatial normalization. The resulting model includes the large bundles of anatomical literature and about 20 U-fiber bundles in each hemisphere.
本文提出了一种从为一组受试者计算的高角分辨率扩散成像（HARDI）纤维束成像结果中推断脑白质组织模型的方法。该模型由一组在大多数人群中都能检测到的通用纤维束组成。我们的方法基于两级聚类策略。第一级是对为每个大脑计算的数百万条纤维束进行多分辨率的受试者内聚类。这种分析将每个受试者的数据复杂性降低到几千条纤维束。第二级是使用空间归一化后计算的成对距离，对所有受试者的纤维束质心进行受试者间聚类。所得模型包括解剖学文献中的大纤维束以及每个半球约20条U形纤维束。
