Robust and efficient linear registration of white-matter fascicles in the space of streamlines
Robust and efficient linear registration of white-matter fascicles in the space of streamlines
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DOI：
10.1016/j.neuroimage.2015.05.016
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发表时间：
2015-08-15
期刊：
影响因子：
5.7
通讯作者：
Descoteaux, Maxime
中科院分区：
文献类型：
--
作者：
Garyfallidis, Eleftherios;Ocegueda, Omar;Descoteaux, Maxime
今天的神经科学界非常有兴趣分析特定的白色物质束，如弓状束，皮质脊髓束，或最近发现的Aslant束，以研究性别差异，侧化和许多其他连接应用。出于这个原因，专家们花费时间使用从扩散MRI纤维束成像获得的流线手动分割这些束和束。然而，到目前为止，很少有计算工具可用于直接登记这些分册，以便可以分析它们并量化它们在人群中的差异。在本文中，我们介绍了一种新的，强大的和有效的框架，直接在流线空间对齐束的流线。我们称这个框架为基于流线的线性配准。我们首先表明，这种方法可以成功地用于调整个别束以及整个大脑流线。此外，如果作为一个分段线性登记在许多束，我们表明，我们的新方法系统地提供了更高的重叠（Jaccard指数）比国家的最先进的非线性基于图像的登记在白色的问题。我们还展示了我们的新方法如何可以用来创建一个简单的方式在特定的地图集，我们给出了一个例子的概率地图集建设的光辐射。总之，基于流线型的线性配准为创建新方法以研究白色物质和执行组级纤维束测量分析提供了坚实的配准框架。(C)2015 Elsevier Inc. All rights reserved.
The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis. (C) 2015 Elsevier Inc. All rights reserved.