Ge Bao, Guo Lei, Zhang Tuo, Zhu Dajiang, Li Kaiming, Hu Xintao, Han Junwei, Liu Tianming
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):485-92. doi: 10.1007/978-3-642-33454-2_60.
Fiber clustering is an essential step towards brain connectivity modeling and tract-based analysis of white matter integrity via diffusion tensor imaging (DTI) in many clinical neuroscience applications. A variety of methods have been developed to cluster fibers based on various types of features such as geometry, anatomy, connection, or function. However, identification of group-wise consistent fiber bundles that are harmonious across multi-modalities is rarely explored yet. This paper proposes a novel hybrid two-stage approach that incorporates connectional and functional features, and identifies group-wise consistent fiber bundles across subjects. In the first stage, based on our recently developed 358 dense and consistent cortical landmarks, we identified consistent backbone bundles with representative fibers. In the second stage, other remaining fibers are then classified into the existing backbone bundles using their correlations of resting state fMRI signals at the two ends of fibers. Our experimental results show that the proposed methods can achieve group-wise consistent fiber bundles with similar shapes and anatomic profiles, as well as strong functional coherences.
在许多临床神经科学应用中，纤维聚类是通过扩散张量成像（DTI）进行脑连接建模和基于纤维束的白质完整性分析的重要步骤。已经开发了多种方法来基于各种类型的特征（如几何形状、解剖结构、连接或功能）对纤维进行聚类。然而，跨多模态协调一致的组内一致纤维束的识别尚未得到充分探索。本文提出了一种新颖的混合两阶段方法，该方法结合了连接和功能特征，并识别跨受试者的组内一致纤维束。在第一阶段，基于我们最近开发的358个密集且一致的皮质地标，我们识别出具有代表性纤维的一致主干纤维束。在第二阶段，然后使用纤维两端静息态功能磁共振成像信号的相关性，将其他剩余纤维分类到现有的主干纤维束中。我们的实验结果表明，所提出的方法可以实现具有相似形状和解剖轮廓以及强功能一致性的组内一致纤维束。
