Neural Tractography Using An Unscented Kalman Filter
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Author(s)
Malcolm, James G.
Shenton, Martha E.
Rathi, Yogesh
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Abstract
We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. 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 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. 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 reduces signal reconstruction error and significantly improves the
angular resolution at crossings and branchings. In vivo experiments confirm the
ability to trace out fibers in areas known to contain such crossing and branching
while providing inherent path regularization.
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Date
2009
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Text
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Post-print