Improved correspondence for DTI population studies via unbiased atlas building.
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.
Goodlett
Casey
C
Department of Computer Science, University of North Carolina, USA.
Davis
Brad
B
Jean
Remi
R
Gilmore
John
J
Gerig
Guido
G
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