Med-X Research Institute, Shanghai Jiao Tong University, Shanghai; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Hum Brain Mapp. 2014 Jul;35(7):3529-46. doi: 10.1002/hbm.22419. Epub 2013 Nov 30.
Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone.
大多数现有的扩散张量成像 (DTI) 配准方法都是基于张量的体素匹配来估计结构对应关系。然而，DTI 提供的丰富连接信息往往被忽视。在本文中，我们提出将连通性特征和张量特征提供的互补信息结合起来，以提高配准精度。为了利用连通性信息，我们在图像空间中放置多个代表不同大脑解剖结构的锚点，并为每个体素定义连通性特征，即从所有锚点到所考虑体素的测地距离。测地距离是相对于张量场计算的，它包含了大脑连接的信息。我们还为每个体素提取张量特征，以反映其邻域中张量的局部统计信息。然后，我们将连通性特征和张量特征结合起来进行张量图像的配准。从图像中自动选择地标，并根据它们的连通性和张量特征向量确定它们的对应关系。根据地标及其相关联的对应关系，迭代地估计和优化变形场，以将一个张量图像变形到另一个张量图像上。实验结果表明，与单独使用任一类特征的情况相比，同时使用连通性特征和张量特征可以显著提高配准精度。
