Introduction and Acknowledgements
Supported by grant number FMECD-2010/71131616E from the Spanish Ministry of Education/Fulbright Committee
This module implements the Finsler tractography method with HARDI data described by J. Melonakos et al. From a set of seeding and target points, the paths are estimated as the shortest path taking into account a local, directional dependent cost.
The output provided is the connectivity map from each voxel in the volume to the seeding points, plus a vector volume with the directions tangent to the fiber bundles at each point. If the Backtracing module within is built, these directions can be traced back to actually compute the fiber bundles
- [[Documentation/Nightly/Modules/FinslerTractography|Finsler Tractography]
Sample data to use with modules.
- G Sharp, N Kandasamy, H Singh, M Folkert, "GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration," Physics in Medicine and Biology, 52(19), pp 5771-83, 2007.
- V Boldea, G Sharp, SB Jiang, D Sarrut, "4D-CT lung motion estimation with deformable registration: Quantification of motion nonlinearity and hysteresis," Medical Physics, 33(3), pp 1008-18, 2008.
- Z Wu, E Rietzel, V Boldea, D Sarrut, G Sharp, "Evaluation of deformable registration of patient lung 4DCT with sub-anatomical region segmentations," Medical Physics, 35(2), pp 775-81, 2008.
- G Sharp et al. "Plastimatch - An open source software suite for radiotherapy image processing," Proceedings of the XVIth International Conference on the use of Computers in Radiotherapy, May, 2010.
- N. Shusharina, G. Sharp "Landmark-based image registration with analytic regularization", IEEE Trans. Med. Imag., submitted, 2011.
Information for Developers
|Section under construction.|