Bach Michael, Laun Frederik B, Leemans Alexander, Tax Chantal M W, Biessels Geert J, Stieltjes Bram, Maier-Hein Klaus H
Section Quantitative Imaging-based Disease Characterization, Department of Radiology, German Cancer Research Center (DKFZ), Germany; Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Germany.
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Neuroimage. 2014 Oct 15;100:358-69. doi: 10.1016/j.neuroimage.2014.06.021. Epub 2014 Jun 16.
Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.
自2006年引入以来，基于纤维束的空间统计学（TBSS）已广受欢迎，如今可被视为扩散张量成像（DTI）数据基于体素分析（VBA）的标准方法。为提高多主体DTI研究的敏感性、客观性和可解释性，TBSS包括一个骨架化步骤，该步骤可减轻残留图像配准误差并消除数据平滑的需求。尽管TBSS是一个优雅且用户友好的框架，解决了传统VBA方法中存在的诸多问题，但它自身也有局限性，其中一些局限性在最近的文献中已有详细阐述。在这项工作中，我们提出了关于TBSS的一般方法学考量，并报告了先前未描述过的陷阱。特别是，我们确定了TBSS的特定假设，这些假设在典型条件下可能无法满足。此外，我们证明了这些违规情况的存在会严重影响TBSS结果的可靠性。随着TBSS的使用越来越多，让TBSS用户了解这些问题至关重要，以便能就是否以及如何进行TBSS分析做出明智的决定。最后，除了通过提供我们的新见解来提高认识外，我们还提供了建设性的建议，这些建议可以极大地提高TBSS的有效性并增加其影响力。
