title: Diffusion of fiber orientation distribution functions with a rotation-induced riemannian metric.
abstract: Advanced diffusion weighted MR imaging allows non-invasive study on the structural connectivity of human brains. Fiber orientation distributions (FODs) reconstructed from diffusion data are a popular model to represent crossing fibers. For this sophisticated image representation of connectivity, classical image operations such as smoothing must be redefined. In this paper, we propose a novel rotation-induced Riemannian metric for FODs, and introduce a weighted diffusion process for FODs regarding this Riemannian manifold. We show how this Riemannian manifold can be used for smoothing, interpolation and building image-pyramids, yielding more accurate or intuitively more reasonable results than the linear or the unit hyper-sphere manifold.
authors.forename: Junning
authors.initials: J
authors.lastname: Li
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Shi
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D012399:Rotation; D012680:Sensitivity and Specificity