STRUCTURAL CONNECTIVITY VIA THE TENSOR-BASED MORPHOMETRY.
STRUCTURAL CONNECTIVITY VIA THE TENSOR-BASED MORPHOMETRY.
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通过基于张量的形态学实现结构连接。
DOI：
10.1109/isbi.2011.5872528
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Pollak,SethD
中科院分区：
文献类型：
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作者：
Kim,Seung-Goo;Chung,MooK;Hanson,JamieL;Avants,BrianB;Gee,JamesC;Davidson,RichardJ;Pollak,SethD
基于张量的形态测量法（TBM）在体素水平上被广泛应用于种群间组织体积差异的表征。我们提出了一种新的计算框架来研究脑白质的连通性。与其他基于扩散张量成像（DTI）的白质连通性研究不同，我们不使用DTI，而只使用t1加权磁共振成像（MRI）。为了构建大脑网络图，我们开发了一种新的数据驱动方法，称为电子邻居方法，它不需要任何预先确定的分割。所提出的管道被应用于检测受虐待儿童白质连接的拓扑改变。
The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.