Computing the probability of local brain connectivity using diffusion tensor imaging

被引：0

作者：

Shimony, Joshua S. ^{[1
]}

Epstein, Adrian A. ^{[1
]}

Bretthorst, G. Larry ^{[2
]}

机构：

[1] Washington Univ, Sch Med, Mallinckrodt Inst Radiol, 521 S Kingshighway Blvd, St Louis, MO 63110 USA

[2] Washington Univ, Sch Med, Dept Chem, St Louis, MO 63110 USA

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2007年 / 954卷

关键词：

diffusion tensor imaging; DTI; MRI; brain connectivity;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The diffusion tensor model has been used to analyze magnetic resonance diffusion data and has been successful in both neuroscientific and clinical applications. We propose an enhancement of this model with a local connectivity parameter that better accords with the known structure of white-matter. In addition to providing diffusion tensor parameter estimation the calculation provides the probability that a given pixel is connected to one of its nearest neighbors. These probabilities can be used in further calculations to determine the probability of connectivity between different brain regions. Implementation of the algorithm is discussed in addition to its usage in simulated and in-vivo magnetic resonance diffusion tensor data.

引用

页码：346 / +

页数：3

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