title: A full bi-tensor neural tractography algorithm using the unscented Kalman filter.
abstract: We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had a cylindrical shape, i.e., the diffusion tensor's second and third eigenvalues were identical. In this paper, we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings, and the tests also confirm the superiority of using a full tensor representation over the simplified model.
authors.affiliation: Computer Vision Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
authors.forename: Stefan
authors.initials: S
authors.lastname: Lienhard
authors.affiliation: Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA, USA.
authors.forename: James G
authors.initials: JG
authors.lastname: Malcolm
authors.affiliation: Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA.
authors.forename: Carl-Frederik
authors.initials: CF
authors.lastname: Westin
authors.affiliation: Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA, USA.
authors.forename: Yogesh
authors.initials: Y
authors.lastname: Rathi