Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-22092-0_37
Title: Large deformation diffeomorphic metric mapping of orientation distribution functions
Authors: Du, J.
Goh, A. 
Qiu, A. 
Keywords: Diffeomorphic mapping
HARDI
Riemannian manifold of ODFs
Issue Date: 2011
Citation: Du, J.,Goh, A.,Qiu, A. (2011). Large deformation diffeomorphic metric mapping of orientation distribution functions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6801 LNCS : 448-462. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-22092-0_37
Abstract: We propose a novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by Orientation Distribution Functions (ODF). Our proposed algorithm seeks an optimal diffeomorphism of large deformation between two ODF fields in a spatial volume domain and at the same time, locally reorients an ODF in a manner such that it remains consistent with the surrounding anatomical structure. We first extend ODFs traditionally defined in a unit sphere to a generalized ODF defined in . This makes it easy for an affine transformation as well as a diffeomorphic group action to be applied on the ODF. We then construct a Riemannian space of the generalized ODFs and incorporate its Riemannian metric for the similarity of ODFs into a variational problem defined under the large deformation diffeomorphic metric mapping (LDDMM) framework. We finally derive the gradient of the cost function in both Riemannian spaces of diffeomorphisms and the generalized ODFs, and present its numerical implementation. Both synthetic and real brain HARDI data are used to illustrate the performance of our registration algorithm. © 2011 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/74884
ISBN: 9783642220913
ISSN: 03029743
DOI: 10.1007/978-3-642-22092-0_37
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