Behrens T E J, Woolrich M W, Jenkinson M, Johansen-Berg H, Nunes R G, Clare S, Matthews P M, Brady J M, Smith S M
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK.
Magn Reson Med. 2003 Nov;50(5):1077-88. doi: 10.1002/mrm.10609.
A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.
提出了一个完全概率框架，用于估计扩散模型中感兴趣参数的局部概率密度函数。该技术应用于扩散张量模型中参数的估计，也应用于一个简单的扩散部分容积模型。在这两种情况下，感兴趣的参数都包括定义局部纤维方向的参数。然后提出了一种利用这些密度函数估计全局连通性的技术（即，在数据场中任意两个远距离点之间存在连接的概率），从而能够对纤维束成像结果的可信度进行量化。该技术随后应用于人类丘脑皮质连通性的估计。所得的连通性分布与非人类灵长类动物侵入性示踪方法的预测结果非常吻合。
