dti-package {dti}R Documentation

Analysis of Diffusion Weighted Imaging (DWI) Data

Description

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.

Details

The DESCRIPTION file:

Package: dti
Version: 1.5.4
Date: 2023-09-06
Title: Analysis of Diffusion Weighted Imaging (DWI) Data
Authors@R: c(person("Karsten", "Tabelow", role = c("aut", "cre"), email = "karsten.tabelow@wias-berlin.de"), person("Joerg", "Polzehl", role = c("aut"), email = "joerg.polzehl@wias-berlin.de"), person("Felix", "Anker", role = c("ctb")))
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Felix Anker [ctb]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>
Depends: R (>= 3.5.0), awsMethods (>= 1.1-1)
SystemRequirements: gsl
Imports: methods, parallel, adimpro (>= 0.9), aws (>= 2.4.1), rgl, oro.nifti (>= 0.3.9), oro.dicom, gsl, quadprog
LazyData: TRUE
Description: 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.
License: GPL (>= 2)
Copyright: This package is Copyright (C) 2005-2020 Weierstrass Institute for Applied Analysis and Stochastics.
URL: https://www.wias-berlin.de/research/ats/imaging/
Suggests: covr
RoxygenNote: 6.1.0

Index of help topics: 

AdjacencyMatrix         Create an adjacency matrix from fiber tracking
                        results
awssigmc                Estimate noise variance for multicoil MR
                        systems
colqFA                  FA map color scheme
combineDWIdata          Combine two objects of class "dtiData")
dkiTensor-methods       Diffusion Kurtosis Imaging (DKI)
dti-package             Analysis of Diffusion Weighted Imaging (DWI)
                        Data
dti.options             Set and manipulate image orientations for
                        plots.
dti.smooth-methods      Methods for Function 'dti.smooth' in Package
                        'dti'
dtiIndices-methods      Methods for Function 'dtiIndices' in Package
                        'dti'
dtiTensor-methods       Methods for Function 'dtiTensor' in Package
                        'dti'
dwi-class               Class "dwi"
dwi.smooth-methods      Smooth DWI data
dwiMD                   Methods for Mean Diffusivity in Package 'dti'
dwiMixtensor-methods    Methods for Function 'dwiMixtensor' in Package
                        'dti'
dwiQball-methods        Methods for Function 'dwiQball' in Package
                        'dti'
dwiRiceBias-methods     Correction for Rician Bias
dwiSqrtODF-methods      Methods for positive definite EAP and ODF
                        estimation in Package 'dti'
extract-methods         Methods for Function 'extract' and '[' in
                        Package 'dti'
getmask-methods         Methods for Function 'getmask' in Package 'dti'
getsdofsb-methods       Estimate the noise standard deviation
medinria                Read/Write Diffusion Tensor Data from/to NIFTI
                        File
optgrad                 Optimal gradient directions
optgradients            Optimal gradient directions for number of
                        gradients between 6 and 162
plot-methods            Methods for Function 'plot' in Package 'dti'
polyeder                Polyeders derived from the Icosahedron (icosa0)
                        by sequential triangulation of surface
                        triangles
print-methods           Methods for Function 'print' in Package 'dti'
readDWIdata             Read Diffusion Weighted Data
sdpar-methods           Methods for Function 'sdpar' in Package 'dti'
setmask-methods         Methods for Function 'setmask' in Package 'dti'
show-methods            Methods for Function 'show' in Package 'dti'
show3d-methods          Methods for Function 'show3d' in Package 'dti'
showFAColorScale        Writes an image with the colqFA colorscale to
                        disk.
subsetg                 Create an objects of class "dtiData" containing
                        only a subset of gradient directions.
summary-methods         Methods for Function 'summary' in Package 'dti'
tracking-methods        Methods for Function 'tracking' in Package
                        'dti'

Author(s)

Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Felix Anker [ctb]

Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>

References

J. Polzehl, K. Tabelow (2019). Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R. Springer, Use R! series. Doi:10.1007/978-3-030-29184-6.

S. Mohammadi, K. Tabelow, L. Ruthotto, Th. Feiweier, J. Polzehl, and N. Weiskopf, High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing, 8 (2015), 427.

S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf, and J. Polzehl, Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS, NeuroImage 95 (2014), pp. 90-105.

S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann and J. Polzehl, Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS), Medical Image Analysis, 16 (2012), pp. 1142-1155.

J. Polzehl and K. Tabelow, Beyond the diffusion tensor model: The package dti, Journal of Statistical Software, 44 no. 12 (2011) pp. 1-26.

K. Tabelow, H.U. Voss and J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Journal of Neuroscience Methods, 203 (2012), pp. 200-211.

J. Polzehl and K. Tabelow, Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software, 31 (2009) pp. 1–24.

K. Tabelow, J. Polzehl, V. Spokoiny and H.U. Voss. Diffusion Tensor Imaging: Structural adaptive smoothing, NeuroImage 39(4), 1763-1773 (2008).

See Also

fmri aws oro.nifti

Examples

  ## Not run: demo(dti_art)
  ## Not run: demo(mixtens_art)
[Package dti version 1.5.4 Index]