Abstract
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.
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Goodlett, C., Davis, B., Jean, R., Gilmore, J., Gerig, G. (2006). Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_32
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DOI: https://doi.org/10.1007/11866763_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44727-6
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