title: Diffusion tensor image registration using hybrid connectivity and tensor features.
abstract: 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.
authors.affiliation: 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.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D066264:Datasets as Topic / Q000706:statistics & numerical data; D056324:Diffusion Tensor Imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology*
keywords: connectivity features; correspondence detection; diffusion tensor image registration; tensor features