Name
Abstract | ||
|---|---|---|
Diffusion tensor imaging can provide the fundamental information required for viewing structural connectivity. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. In this paper, we present a novel algorithm for extracting and visualizing the fiber tracts in the CNS, specifically in the brain. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing of the diffusion-weighted data (prior to tensor calculation) is achieved via a weighted TV-norm minimization, which strives to smooth while retaining all relevant detail. For the fiber tract mapping, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Neuronal fibers are then traced by calculating the integral curves of this vector field. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.media.2003.12.001 | Medical Image Analysis |
Keywords | Field | DocType |
DTI,DT-MRI,Diffusion tensor,Total variation | Computer vision,Euclidean vector,Diffusion MRI,Fiber,Pattern recognition,Tensor,Vector field,Visualization,Smoothing,Artificial intelligence,Line integral convolution,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 2 | 1361-8415 |
Citations | PageRank | References |
29 | 2.95 | 20 |
Authors | ||
5 | ||