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dti (version 0.8-0)

DTI/DWI Analysis

Description

Diffusion Weighted Imaging 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 in the context of the diffusion tensor model (DTI). This includes the calculation of anisotropy measures and, most important, the implementation of our structural adaptive smoothing algorithm as described in K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss, Diffusion Tensor Imaging: Structural Adaptive Smoothing, Neuroimage 39(4), 1763-1773 (2008).

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Version

Install

install.packages('dti')

Monthly Downloads

225

Version

0.8-0

License

GPL (>= 2)

Maintainer

Karsten Tabelow

Last Published

April 15th, 2009

Functions in dti (0.8-0)

dtiIndices-methods

Methods for Function `dtiIndices' in Package `dti'

dti.smooth-methods

Methods for Function `dti.smooth' in Package `dti'

Methods for Function `plot' in Package `dti'

Methods for Function `show3d' in Package `dti'

Methods for Function `show' in Package `dti'

summary-methods

Methods for Function `summary' in Package `dti'

dwiQball-methods

Methods for Function `dwiQball' in Package `dti'

Methods for Function `sdpar' in Package `dti'

dtiTensor-methods

Methods for Function `dtiTensor' in Package `dti'

Read/Write Diffusion Tensor Data from/to NIFTI File

Read Diffusion Weighted Data

Methods for Function `print' in Package `dti'

extract-methods

Methods for Function `extract' and `[' in Package `dti'

Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles