http://www.sciencedirect.com/science/article/pii/S1053811913010239
Highlights
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- Dynamic programming (DP) was applied to tractography based on DTI data.
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- DP finds the most probable path between two specified brain regions.
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- DP delineates large tracts that could not be reconstructed by streamline methods.
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- For accurate path generation, knowledge-based ROI sets are built.
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- Fifty two tracts were reconstructed in the MNI space for white matter atlases.
Abstract
It
has been shown that the anatomy of major white matter tracts can be
delineated using diffusion tensor imaging (DTI) data. Tract
reconstruction, however, often suffers from a large number of
false-negative results when a simple line propagation algorithm is used.
This limits the application of this technique to only the core of
prominent white matter tracts. By employing probabilistic
path-generation algorithms, connectivity between a larger number of
anatomical regions can be studied, but an increase in the number of
false-positive results is inevitable. One of the causes of the
inaccuracy is the complex axonal anatomy within a voxel; however,
high-angular resolution (HAR) methods have been proposed to ameliorate
this limitation. However, HAR data are relatively rare due to the long
scan times required and the low signal-to-noise ratio. In this study, we
tested a probabilistic path-finding method in which two anatomical
regions with known connectivity were pre-defined and a path that
maximized agreement with the DTI data was searched. To increase the
accuracy of the trajectories, knowledge-based anatomical constraints
were applied. The reconstruction protocols were tested using DTI data
from 19 normal subjects to examine test–retest reproducibility and
cross-subject variability. Fifty-two tracts were found to be reliably
reconstructed using this approach, which can be viewed on our website.
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