Package dti details

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), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.

Maintainer: Karsten Tabelow < karsten.tabelow at >

From within R, enter citation(dti)

To cite dti in publications use:

Karsten Tabelow, Joerg Polzehl (2011). Beyond the Gaussian Model in
Diffusion-Weighted Imaging: The Package dti. Journal of Statistical
Software, 44(12), 1-26. URL

For the structural adaptive smoothing methods please cite:

Karsten Tabelow, Joerg Polzehl, Vladimir Spokoiny, Henning U. Voss
(2008). Diffusion Tensor Imaging: Structural Adaptive Smoothing.
NeuroImage, 39(4), 1763-1773. doi:10.1016/j.neuroimage.2007.10.024

For the msPOAS method please cite:

Saskia M.A. Becker, Karsten Tabelow, Siawoosh Mohammadi, Nikolaus
Weiskopf, Joerg Polzehl (2014). Adaptive smoothing of multi-shell
diffusion-weighted magnetic resonance data by msPOAS. NeuroImage, 95,
90-105. doi:10.1016/j.neuroimage.2014.03.053

For the tensor mixture model please cite:

Karsten Tabelow, Henning U. Voss, Joerg Polzehl (2012). Modeling the
orientation distribution function by mixtures of angular central
Gaussian distributions. Journal of Neuroscience Methods, 203(1),
200-211. doi:10.1016/j.jneumeth.2011.09.001

To see these entries in BibTeX format, use 'print(,
bibtex=TRUE)', 'toBibtex(.)', or set


If you have any problems with this package you can open a new issue or check the already existing ones here.

To install this package, start R and enter:


# Default Install

# From the Binary Repo in NeuroC
neuro_install('dti', release = "stable", release_repo = binary_release_repo(release = "stable"))
neuro_install('dti', release = "current", release_repo = binary_release_repo(release = "current"))

# from GitHub
neuro_install('dti', release = "stable", release_repo = "github")
neuro_install('dti', release = "current", release_repo = "github")

More detailed installation instructions can be found here.


Initially submitted on October 1 2018 4:57PM
Last updated on May 9 2019 12:02AM
Package type standard
Source GitHub GitHub
Neuroconductor GitHub GitHub
System requirementsgsl
DependsR (3.1.0), awsMethods(>=1.0-1), adimpro, rgl
Importsmethods, parallel, oro.nifti (0.3.9), oro.dicom, gsl, quadprog