Image Processing Reference
In-Depth Information
FIGURE 13.15 Degree of connectivity, measured as Euclidean path length over geodesic
path length. Very low connectivity is not shown. Purple denotes the highest connectivity.
Traditional tractography based on following the principal eigenvector direction, with seed
locations around the initial point, is displayed in red (right). Visual inspection confirms
that the trace lines agree well with the region of highest connectivity. (From O'Donnell,
L., Haker, S., and Westin, C.-F. (2002). New approaches to estimation of white matter
connectivity in diffusion tensor MRI: Elliptic PDEs and geodesics in a tensor-warped
space. in Dohi, T. and Kikinis, R. (Eds.). Medical Image Computing and Computer-
Assisted Intervention (MICCAI) . pp. 459-466, Tokyo, Japan.)
Finally, we introduced two novel image processing methods for connectivity
estimation. In the first method, we solved for a steady-state heat distribution and
flow field that reflect connectivity. In the second method, the introduction of a
Riemannian metric allowed us to reformulate the connectivity/diffusion simula-
tion problem as a search for geodesic paths.
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