Goodlett Casey, Davis Brad, Jean Remi, Gilmore John, Gerig Guido
Department of Computer Science, University of North Carolina, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):260-7. doi: 10.1007/11866763_32.
We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.
我们提出了一种方法，用于通过将扩散张量成像（DTI）与通用图谱进行可变形配准来自动找到对应关系。该配准联合生成一个平均DTI图谱，该图谱对于模板图像的选择是无偏的，同时还生成每个图像之间的微分同胚对应关系。配准图像匹配度量使用用于白质细纤维结构的特征检测器，并且扩散张量的插值和平均使用黎曼对称空间框架。具有解剖学意义的对应关系为临床研究中张量特征和纤维束几何形状的比较以及构建DTI群体图谱提供了基础。
