Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm

2016 ◽  
Vol 73 ◽  
pp. 14-22 ◽  
Author(s):  
Tahereh Kamali ◽  
Daniel Stashuk
2016 ◽  
Vol 6 (10) ◽  
pp. 747-758 ◽  
Author(s):  
Sean R. McWhinney ◽  
Antoine Tremblay ◽  
Thérèse M. Chevalier ◽  
Vanessa K. Lim ◽  
Aaron J. Newman

CNS Spectrums ◽  
2002 ◽  
Vol 7 (7) ◽  
pp. 529-534 ◽  
Author(s):  
Susumu Mori

ABSTRACTThe raw diffusion tensor imaging data obtained after tensor calculations contain six tensor elements in each pixel. This unique data structure poses difficulties in visualizing and analyzing diffusion tensor imaging data. One of the most commonly used visualization techniques is the use of color-coded maps. This presents fiber orientation information as a mixture of three principal colors. These maps can reveal white matter substructures that may not be visible in conventional magnetic resonance imaging. By extending the fiber-orientation information into three-dimensional space, three-dimensional trajectories of white matter tracts can then be estimated. Once locations and trajectories of tracts of interest are identified, this technique allows for the utilization of tract-specific magnetic resonance analyses and/or macroscopic characterization of white matter anatomy. As an example, anatomical deformation of the white matter resultant of brain tumor is demonstrated. The potentials and limitations of the three-dimensional tract reconstruction techniques are also highlighted.


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