Gigandet Xavier, Hagmann Patric, Kurant Maciej, Cammoun Leila, Meuli Reto, Thiran Jean-Philippe
Signal Processing Laboratory, LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
PLoS One. 2008;3(12):e4006. doi: 10.1371/journal.pone.0004006. Epub 2008 Dec 23.
Since the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. For example, it is difficult to assess whether a connection is the result of the diffusion coherence contrast itself or the simple result of other uncontrolled parameters like for example: noise, brain geometry and algorithmic characteristics.
METHODOLOGY/PRINCIPAL FINDINGS: In this work, we propose a method to estimate the respective contributions of diffusion coherence versus other effects to a tractography result by comparing data sets with and without diffusion coherence contrast. We use this methodology to assign a confidence level to every gray matter to gray matter connection and add this new information directly in the connectivity matrix.
CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that whereas we can have a strong confidence in mid- and long-range connections obtained by a tractography experiment, it is difficult to distinguish between short connections traced due to diffusion coherence contrast from those produced by chance due to the other uncontrolled factors of the tractography methodology.
自扩散张量成像出现以来，人们开展了大量工作以更好地理解扩散磁共振成像纤维束成像的特性。然而，重建纤维连接的验证在很多方面仍然存在问题。例如，很难评估一种连接是扩散相干对比度本身的结果，还是其他未控制参数（如噪声、脑几何形状和算法特征）的简单结果。
方法/主要发现：在这项工作中，我们提出了一种方法，通过比较有无扩散相干对比度的数据集，来估计扩散相干与其他效应在纤维束成像结果中的各自贡献。我们使用这种方法为每个灰质到灰质的连接分配一个置信水平，并将这个新信息直接添加到连通性矩阵中。
结论/意义：我们的结果表明，虽然我们可以对通过纤维束成像实验获得的中长程连接有很强的信心，但很难区分由于扩散相干对比度追踪到的短连接与由于纤维束成像方法的其他未控制因素偶然产生的短连接。
