Li Hai, Xue Zhong, Guo Lei, Wong Stephen T C
The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX, USA.
Inf Process Med Imaging. 2009;21:63-75. doi: 10.1007/978-3-642-02498-6_6.
Diffusion tensor imaging (DTI) plays increasingly important roles in surgical planning, neurological disease diagnosis, and follow-up studies in recent years. In order to compare the tractography obtained from different subjects or the same subject at different timepoints, a key step is to spatially align DTI images. Different from scalar or multi-channel image registration, tensor orientation should be considered in DTI registration. Several DTI registration methods have been proposed before, and some of them are based on first extracting the orientation-invariant features and then registering images using traditional scalar or multi-channel registration techniques followed by tensor reorientation. They essentially do not fully use the tensor information. Other methods such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms use analytical gradients of the registration objective functions by considering the reorientation of tensor during the registration. However, only local tensor information such as voxel tensor similarity is utilized in these algorithms, which can be regarded as a counterpart of the traditional intensity similarity-based image registration in the DTI case. This paper proposes a novel DTI image registration algorithm, called fast marching-based simultaneous registration. It not only considers the orientation of tensors but also utilizes the neighborhood tensor information of each voxel, which is extracted from a local fast marching algorithm around voxels of interest. Compared to the voxel-wise tensor similarity-based registration, richer and more distinctive tensor features are used in this algorithm to better define correspondences between DTI images. Thus, more robust and accurate registration results can be obtained. In the experiments, comparative results using the real DTI data show the advantages of the proposed algorithm.
近年来，扩散张量成像（DTI）在手术规划、神经疾病诊断及随访研究中发挥着越来越重要的作用。为了比较不同受试者或同一受试者在不同时间点获得的纤维束成像，关键步骤是对DTI图像进行空间对齐。与标量或多通道图像配准不同，DTI配准中应考虑张量方向。此前已提出了几种DTI配准方法，其中一些方法是先提取方向不变特征，然后使用传统的标量或多通道配准技术配准图像，随后进行张量重新定向。它们本质上没有充分利用张量信息。其他方法，如逐片仿射变换和微分同胚非线性配准算法，在配准过程中通过考虑张量的重新定向来使用配准目标函数的解析梯度。然而，这些算法仅利用了体素张量相似性等局部张量信息，在DTI情况下可视为传统基于强度相似性的图像配准的对应方法。本文提出了一种新的DTI图像配准算法，称为基于快速行进的同步配准。它不仅考虑张量的方向，还利用每个体素的邻域张量信息，该信息是从感兴趣体素周围的局部快速行进算法中提取的。与基于体素张量相似性的配准相比，该算法使用了更丰富、更独特的张量特征来更好地定义DTI图像之间的对应关系。因此，可以获得更稳健、准确的配准结果。在实验中，使用真实DTI数据的比较结果显示了所提算法的优势。
