Filtered Tractography: State estimation in a constrained subspace

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Date
2009-09-24Author
Malcolm, James G.
Shenton, Martha E.
Rathi, Yogesh
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Show full item recordAbstract
We describe amethod of deterministic tractography using model-based
estimation that remains constrained to the subspace of valid tensor mixture models.
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 a weighted mixture of Gaussian tensors 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, thereby
constraining it to a subspace of allowable model parameters. 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. We conclude by applying unsupervised
clustering to provide side-by-side comparison of the models.