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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Goodlett, C., Davis, B., Jean, R., Gilmore, J., & Gerig, G. (2006). Improved correspondence for DTI population studies via unbiased atlas building. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4191 LNCS-II, pp. 260–267). Springer Verlag. https://doi.org/10.1007/11866763_32
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