使用结构连接信息评估脑容量配准性能。
Evaluating volumetric brain registration performance using structural connectivity information.
作者信息
Petrović Aleksandar, Zöllei Lilla
机构信息
University of Oxford, FMRIB Centre, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
出版信息
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):524-31. doi: 10.1007/978-3-642-23629-7_64.
In this paper, we propose a pipeline for evaluating the performance of brain image registration methods. Our aim is to compare how well the algorithms align subtle functional/anatomical boundaries that are not easily detectable in T1- or T2-weighted magnetic resonance images (MRI). In order to achieve this, we use structural connectivity information derived from diffusion-weighted MRI data. We demonstrate the approach by looking into how two competing registration algorithms perform at aligning fine-grained parcellations of subcortical structures. The results show that the proposed evaluation framework can offer new insights into the performance of registration algorithms in brain regions with highly varied structural connectivity profiles.
在本文中，我们提出了一种用于评估脑图像配准方法性能的流程。我们的目的是比较这些算法在对齐T1加权或T2加权磁共振成像（MRI）中不易检测到的细微功能/解剖边界方面的表现。为了实现这一点，我们使用从扩散加权MRI数据中得出的结构连接信息。我们通过研究两种相互竞争的配准算法在对齐皮质下结构的细粒度脑区划分方面的表现来展示该方法。结果表明，所提出的评估框架可以为具有高度不同结构连接特征的脑区中配准算法的性能提供新的见解。
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