R package dti: Analysis of Diffusion Weighted Imaging (DWI) Data. Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), structural adaptive smoothing in in case of (DTI) (K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss, Diffusion Tensor Imaging: Structural Adaptive Smoothing, Neuroimage 39(4), 1763-1773 (2008)), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
- Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018) not zbMATH
- Carmichael, Owen; Chen, Jun; Paul, Debashis; Peng, Jie: Diffusion tensor smoothing through weighted Karcher means (2013)
- Yu, Tao; Zhang, Chunming; Alexander, Andrew L.; Davidson, Richard J.: Local tests for identifying anisotropic diffusion areas in human brain with DTI (2013)
- Jonathan Clayden; Susana Maniega; Amos Storkey; Martin King; Mark Bastin; Chris Clark: TractoR: Magnetic Resonance Imaging and Tractography with R (2011) not zbMATH
- Jörg Polzehl; Karsten Tabelow: Beyond the Gaussian Model in Diffusion-Weighted Imaging: The Package dti (2011) not zbMATH
- Karsten Tabelow; Brandon Whitcher: Special Volume on Magnetic Resonance Imaging in R (2011) not zbMATH