Wassermann Demian, Rathi Yogesh, Bouix Sylvain, Kubicki Marek, Kikinis Ron, Shenton Martha, Westin Carl-Fredrik
Laboratory of Mathematics in Imaging, Brigham & Women's Hospital, Boston, MA, USA.
Inf Process Med Imaging. 2011;22:320-32. doi: 10.1007/978-3-642-22092-0_27.
This paper proposes a method for the registration of white matter tract bundles traced from diffusion images and its extension to atlas generation, Our framework is based on a Gaussian process representation of tract density maps. Such a representation avoids the need for point-to-point correspondences, is robust to tract interruptions and reconnections and seamlessly handles the comparison and combination of white matter tract bundles. Moreover, being a parametric model, this approach has the potential to be defined in the Gaussian processes' parameter space, without the need for resampling the fiber bundles during the registration process. We use the similarity measure of our Gaussian process framework, which is in fact an inner product, to drive a diffeomorphic registration algorithm between two sets of homologous bundles which is not biased by point-to-point correspondences or the parametrization of the tracts. We estimate a dense deformation of the underlying white matter using the bundles as anatomical landmarks and obtain a population atlas of those fiber bundles. Finally we test our results in several different bundles obtained from in-vivo data.
本文提出了一种用于对从扩散图像追踪得到的白质纤维束进行配准的方法及其在图谱生成方面的扩展。我们的框架基于纤维束密度图的高斯过程表示。这种表示避免了点对点对应关系的需求，对纤维束的中断和重新连接具有鲁棒性，并且能够无缝处理白质纤维束的比较和组合。此外，作为一种参数模型，该方法有潜力在高斯过程的参数空间中进行定义，而无需在配准过程中对纤维束进行重新采样。我们使用高斯过程框架的相似性度量（实际上是一种内积）来驱动两组同源纤维束之间的微分同胚配准算法，该算法不受点对点对应关系或纤维束参数化的影响。我们将纤维束用作解剖学标志来估计潜在白质的密集变形，并获得这些纤维束的群体图谱。最后，我们在从体内数据获得的几个不同纤维束中测试了我们的结果。
