Wang Qian, Yap Pew-Thian, Wu Guorong, Shen Dinggang
Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):200-8. doi: 10.1007/978-3-642-23629-7_25.
Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective correspondence matching. The attribute vector captures complementary information from both the global connectivity structure given by the fiber tracts and the local anatomical architecture given by the tensor regional descriptors. We incorporate this attribute vector into a multi-scale registration framework where the moving image is warped to the space of the fixed image under the guidance of tract information at a more global level (coarse scales), followed by alignment refinement using regional tensor distribution features at a more local level (fine scales). Experimental results indicate that this framework yields marked improvement over DT image registration using volumetric information alone.
扩散张量（DT）图像的配准是必不可少的，特别是在涉及大量数据的白质研究中。然而，这项任务面临着一些具有挑战性的问题，例如扩散加权图像的信噪比普遍较低，以及张量表示的相对较高的复杂性。为了提高DT图像配准的准确性，我们设计了一种属性向量，它封装了纤维束和张量信息，作为有效的对应匹配的体素形态特征。该属性向量从纤维束给出的全局连通性结构和张量区域描述符给出的局部解剖结构中捕获互补信息。我们将此属性向量纳入多尺度配准框架，其中在更全局的层面（粗尺度）上，在纤维束信息的引导下，将移动图像扭曲到固定图像的空间，然后在更局部的层面（细尺度）上使用区域张量分布特征进行对齐细化。实验结果表明，该框架相对于仅使用体积信息的DT图像配准有显著改进。
