Diffusion tensor-based fast marching for modeling human brain connectivity network.
Diffusion tensor-based fast marching for modeling human brain connectivity network.
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DOI：
10.1016/j.compmedimag.2010.07.008
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发表时间：
2011-04
影响因子：
5.7
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Li, Hai;Xue, Zhong;Cui, Kemi;Wong, Stephen T. C.
扩散张量成像（DTI）是研究大脑连通性的有效方法。为了消除由于DTI的空间分辨率和获得的纤维数量低而导致的纤维提取方法可能造成的偏差，提出了基于全扩散张量信息的快速行进（FM）算法来建模和研究大脑连接网络。我们的观察是，从整个张量场提取的连接将是更强大和可靠的使用DTI数据构建大脑连接网络。为了构建连通性网络，本文将FM算法生成的到达时间图和速度图相结合，以定义不同脑区之间的连通性强度。将传统的基于纤维跟踪的FM连通性方法与本文提出的基于张量的FM连通性方法进行了比较，结果表明基于FM的方法得到的连通性特征更符合人脑神经形态学研究的结果。
Diffusion tensor imaging (DTI) is an effective modality in studying the connectivity of the brain. To eliminate possible biases caused by fiber extraction approaches due to low spatial resolution of DTI and the number of fibers obtained, the fast marching (FM) algorithm based on the whole diffusion tensor information is proposed to model and study the brain connectivity network. Our observation is that the connectivity extracted from the whole tensor field would be more robust and reliable for constructing brain connectivity network using DTI data. To construct the connectivity network, in this paper, the arrival time map and the velocity map generated by the FM algorithm are combined to define the connectivity strength among different brain regions. The conventional fiber tracking-based and the proposed tensor-based FM connectivity methods are compared, and the results indicate that the connectivity features obtained using the FM-based method agree better with the neuromorphical studies of the human brain.
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影响因子：
3.7
作者：
Hagmann, Patric;Kurant, Maciej;Gigandet, Xavier;Thiran, Patrick;Wedeen, Van J.;Meuli, Reto;Thiran, Jean-Philippe
通讯作者：
Thiran, Jean-Philippe
影响因子：
5.7
作者：
Iturria-Medina, Yasser;Sotero, Roberto C.;Melie-Garcia, Lester
通讯作者：
Melie-Garcia, Lester
DOI：
10.1006/jmrb.1994.1037
发表时间：
1994-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者：
LEBIHAN, D
影响因子：
3.7
作者：
Felleman, Daniel J.;Van Essen, David C.
通讯作者：
Van Essen, David C.
影响因子：
5.7
作者：
Liu, Tianming;Li, Hai;Wong, Stephen T. C.
通讯作者：
Wong, Stephen T. C.