Jin Yan, Shi Yonggang, Zhan Liang, Li Junning, de Zubicaray Greig I, McMahon Katie L, Martin Nicholas G, Wright Margaret J, Thompson Paul M
Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
University of Queensland, Brisbane St. Lucia, QLD 4072, Australia.
Multimodal Brain Image Anal (2012). 2012 Jan 1;7509:147-156. doi: 10.1007/978-3-642-33530-3_12.
Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.
在扩散加权脑磁共振成像中对白质纤维进行自动标记对于比较不同人群的脑完整性和连通性至关重要，但具有挑战性。全脑纤维束成像在整个大脑中生成大量纤维，但由于白质通路的轨迹和形状存在广泛的个体差异，很难将它们聚类成具有解剖学意义的纤维束。我们提出了一种新颖的自动纤维束标记算法，该算法融合了纤维束成像和多个手工标记的纤维束图谱的信息。由于流线型纤维束成像会产生大量假阳性纤维，我们基于与多个手工标记图谱的距离度量，开发了一种自上而下的方法来提取与已知解剖结构一致的纤维束。使用多阶段融合方案融合来自不同图谱的聚类结果。我们的“标签融合”方法从100名年轻正常成年人的105梯度高分辨率扩散成像（HARDI）扫描中可靠地提取了主要纤维束。
