Projects:MultiTensorTractography

Multi Tensor Tractography

We describe a unified framework to simultaneously estimate multiple fibers at each location and perform tractography. Existing techniques estimate the local fiber orientation at each voxel

 independently so there is no running knowledge of confidence in the
 estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing
 the fiber, the current estimate is guided by the previous.

 To do this we model the signal as either a weighted mixture of Gaussian tensors or
 Watson directional functions and
 perform tractography within a filter framework.  Starting from a seed point,
 each fiber is traced to its termination using an unscented Kalman filter to
 simultaneously fit the local model and propagate in the most consistent
 direction.  Further, we modify the Kalman filter to enforce model
 constraints, i.e., positive eigenvalues and convex weights.  Despite the
 presence of noise and uncertainty, this provides a causal estimate of the
 local structure at each point along the fiber.
 
 Synthetic experiments demonstrate that this approach significantly improves
 the angular resolution at crossings and branchings while consistently
 estimating the mixture weights.  In vivo experiments confirm the
 ability to trace out fibers in areas known to contain such crossing and
 branching while providing inherent path regularization.