Schilling Kurt G, Gao Yurui, Stepniewska Iwona, Janve Vaibhav, Landman Bennett A, Anderson Adam W
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Magn Reson Imaging. 2019 Jan;55:7-25. doi: 10.1016/j.mri.2018.09.004. Epub 2018 Sep 10.
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
二十年来，扩散纤维束成像技术一直被用于探究白质通路的空间范围以及大脑区域间的连通性。在这两种情况下，纤维束成像的解剖学准确性对于得出可靠的科学结论至关重要。在此，我们评估并验证了最广泛使用的算法和纤维束成像实现方式——通常是因为其易用性、算法简单性或开源软件中提供的可用性。将四十个纤维束成像结果与同一松鼠猴大脑初级运动皮层中由组织学示踪剂定义的真实情况进行比较，我们在体素尺度以及更大的空间域或区域连通性上评估纤维束保真度。没有一种算法在所有指标上都成功，事实上，一些实现方式无法重建大部分通路或识别主要的连通点。准确性最依赖于重建方法和追踪算法，以及种子区域和该区域的使用方式。我们还注意到，尽管相同的磁共振图像作为所有算法的输入，但结果存在巨大差异。此外，离种子的距离增加时，解剖学准确性会显著降低。对纤维束成像中的空间误差进行分析表明，许多技术在正确离开灰质方面存在困难，而且许多技术仅揭示与相邻感兴趣区域的连通性。这些结果表明，最常用的算法存在若干缺点和局限性，实现方式的选择会导致非常不同的结果。这项研究应为基于敏感性、特异性研究要求或识别特定连接需求的算法选择提供指导，并应为纤维束成像的未来发展提供启发。
