dti-package: Analysis of Diffusion Weighted Imaging (DWI) Data

In neuroconductor/dti: 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

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Author(s)

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

Maintainer: Karsten Tabelow <[e-mail]>

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)

neuroconductor/dti documentation built on May 20, 2021, 4:28 p.m.