title: Automatic deformable diffusion tensor registration for fiber population analysis.
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.
authors.affiliation: Computer Sciences and Engineering Department, The Ohio State University, USA. irfanoglu.1@osu.edu
authors.forename: M O
authors.initials: MO
authors.lastname: Irfanoglu
authors.forename: R
authors.initials: R
authors.lastname: Machiraju
authors.forename: S
authors.initials: S
authors.lastname: Sammet
authors.forename: C
authors.initials: C
authors.lastname: Pierpaoli
authors.forename: M V
authors.initials: MV
authors.lastname: Knopp
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique