Local white matter geometry from diffusion tensor gradients.
We introduce a mathematical framework for computing geometrical properties of white matter fibers directly from diffusion tensor fields. The key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. The resulting eigenframe-centered representation then makes it possible to define scalar indices (or measures) that describe the local white matter geometry directly from the diffusion tensor field and its gradient, without requiring prior tractography. We derive new scalar indices of (1) fiber dispersion and (2) fiber curving, and we demonstrate them on synthetic and in vivo data. Finally, we illustrate their applicability to a group study on schizophrenia.
Savadjiev
Peter
P
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. petersv@bwh.harvard.edu
Kindlmann
Gordon L
GL
Bouix
Sylvain
S
Shenton
Martha E
ME
Westin
Carl-Fredrik
CF
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; 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*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity