title: Improved correspondence for DTI population studies via unbiased atlas building.
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.
authors.affiliation: Department of Computer Science, University of North Carolina, USA.
authors.forename: Casey
authors.initials: C
authors.lastname: Goodlett
authors.forename: Brad
authors.initials: B
authors.lastname: Davis
authors.forename: Remi
authors.initials: R
authors.lastname: Jean
authors.forename: John
authors.initials: J
authors.lastname: Gilmore
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
mesh_terms: D000465:Algorithms; D000716:Anatomy, Artistic / Q000379:methods; D001185:Artificial Intelligence; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008488:Medical Illustration; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009434:Neural Pathways / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity