通过随机迭代球面反卷积引导的纤维束图滤波评估流线合理性
Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering
AI总结：
针对 SIFT 依赖整体纤维束图、难以评估单条流线合理性的问题，本文提出随机子集迭代滤波并训练分类器，实现超过80%准确率的合理性判别。
摘要：
纤维束成像已成为脑连接研究中不可或缺的一部分。然而，它目前正面临可靠性问题。具体而言，由最先进的纤维束成像方法生成的纤维束图中，大量神经纤维重建结果（流线）在解剖学上并不合理。为解决这一问题，研究者开发了纤维束图滤波方法，以在后处理步骤中去除错误连接。本研究更仔细地考察了其中一种方法，即 Spherical-deconvolution Informed Filtering of Tractograms（SIFT，球面反卷积引导的纤维束图滤波），该方法采用全局优化方式，提高滤波后剩余流线与底层弥散磁共振成像数据之间的一致性。SIFT 不适合判断单条流线的合理性，因为其结果取决于周围纤维束图的规模和组成。为解决该问题，我们建议将 SIFT 应用于随机选择的纤维束图子集，从而为每条流线获得多次评估。该方法能够识别滤波结果高度一致的流线，这些流线被用作训练分类器的伪真值。训练后的分类器能够以超过80%的准确率区分所得的合理与不合理流线组。本文使用的软件代码和分类器预训练权重通过 Github 仓库 https://github.com/djoerch/randomised_filtering 免费发布。
英文摘要：
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, \textit{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 to judge the plausibility of individual streamlines 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 plausible and implausible streamlines with accuracy above 80%. The software code used in the paper and pretrained weights of the classifier are distributed freely via the Github repository https://github.com/djoerch/randomised_filtering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
