Saarland University, Faculty of Mathematics and Computer Science, Campus E1.7, Saarbruecken, 66041, Saarland, Germany.
Division of Brain, Imaging, and Behaviour, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Huddinge, 14157, Stockholm, Sweden.
Neuroimage. 2023 Sep;278:120248. doi: 10.1016/j.neuroimage.2023.120248. Epub 2023 Jul 8.
Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.
轨迹追踪已经成为脑连接研究不可或缺的一部分。然而，它目前面临着可靠性问题。特别是，最先进的轨迹追踪方法生成的轨迹追踪中，大量的神经纤维重建（轨迹线）在解剖学上是不合理的。为了解决这个问题，已经开发出了轨迹追踪过滤方法，以便在后处理步骤中去除错误的连接。本研究更详细地研究了一种这样的方法，即轨迹追踪的球形反卷积信息过滤（SIFT），它使用全局优化方法来提高过滤后剩余轨迹线与基础扩散磁共振成像数据之间的一致性。SIFT 不适合判断单个轨迹线与所获得数据的一致性，因为其结果取决于周围轨迹追踪的大小和组成。为了解决这个问题，我们建议将 SIFT 应用于随机选择的轨迹追踪子集，以便为每条轨迹线检索多个评估。这种方法可以识别过滤结果非常一致的轨迹线，这些轨迹线被用作训练分类器的伪真实数据。训练有素的分类器能够以超过 80%的准确率区分与所获得数据相符和不相符的轨迹线组。
