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Automatic deformable diffusion tensor registration for fiber population analysis.

Author information

1
Computer Sciences and Engineering Department, The Ohio State University, USA. irfanoglu.1@osu.edu

Abstract

In this work, we propose a novel method for deformable tensor-to-tensor registration of Diffusion Tensor Images. Our registration method models the distances in between the tensors with Geode-sic-Loxodromes and employs a version of Multi-Dimensional Scaling (MDS) algorithm to unfold the manifold described with this metric. Defining the same shape properties as tensors, the vector images obtained through MDS are fed into a multi-step vector-image registration scheme and the resulting deformation fields are used to reorient the tensor fields. Results on brain DTI indicate that the proposed method is very suitable for deformable fiber-to-fiber correspondence and DTI-atlas construction.

PMID:
18982704
PMCID:
PMC4819430
[Indexed for MEDLINE]
Free PMC Article
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