Martinos Center for Biomedical Imaging, MGH, Boston, MA, USA.
Neuroimage. 2010 May 15;51(1):206-13. doi: 10.1016/j.neuroimage.2010.01.101. Epub 2010 Feb 12.
Previously we introduced an automated high-dimensional non-linear registration framework, CVS, that combines volumetric and surface-based alignment to achieve robust and accurate correspondence in both cortical and sub-cortical regions (Postelnicu et al., 2009). In this paper we show that using CVS to compute cross-subject alignment from anatomical images, then applying the previously computed alignment to diffusion weighted MRI images, outperforms state-of-the-art techniques for computing cross-subject alignment directly from the DWI data itself. Specifically, we show that CVS outperforms the alignment component of TBSS in terms of degree-of-alignment of manually labeled tract models for the uncinate fasciculus, the inferior longitudinal fasciculus and the corticospinal tract. In addition, we compare linear alignment using FLIRT based on either fractional anisotropy or anatomical volumes across-subjects, and find a comparable effect. Together these results imply a clear advantage to aligning anatomy as opposed to lower resolution DWI data even when the final goal is diffusion analysis.
之前我们介绍了一种自动化的高维非线性配准框架 CVS，它结合了基于体积和表面的配准方法，以实现皮质和皮质下区域的强大而准确的对应关系（Postelnicu 等人，2009 年）。在本文中，我们展示了使用 CVS 从解剖图像计算跨受试者配准，然后将之前计算的配准应用于扩散加权 MRI 图像，可以优于直接从 DWI 数据本身计算跨受试者配准的最先进技术。具体来说，我们表明，在手动标记束模型的吻合度方面，CVS 优于 TBSS 的配准组件，用于钩束、下纵束和皮质脊髓束。此外，我们还比较了基于分数各向异性或解剖体积的跨受试者使用基于 FLIRT 的线性配准，并发现了类似的效果。这些结果共同表明，与使用较低分辨率的 DWI 数据进行配准相比，即使最终目标是扩散分析，对齐解剖结构也具有明显的优势。
