Fast and reproducible fiber bundle selection in DTI visualization

J Blaas, CP Botha, B Peters, FM Vos… - VIS 05. IEEE …, 2005 - ieeexplore.ieee.org
J Blaas, CP Botha, B Peters, FM Vos, FH Post
VIS 05. IEEE Visualization, 2005., 2005ieeexplore.ieee.org
Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in
living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal
axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used
to determine trajectories of fiber bundles, or neuronal connections between regions, in the
brain. The resulting bundles can be visualized. However, the resulting visualizations can be
complex and difficult to interpret. An effective approach is to pre-determine trajectories from …
Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and difficult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles.
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