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dti (version 0.6-0)

DTI 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

405

Version

0.6-0

License

GPL (>=2)

Maintainer

Karsten Tabelow

Last Published

September 26th, 2024

Functions in dti (0.6-0)

plot-methods

Methods for Function `plot' in Package `dti'
dtiTensor-methods

Methods for Function `dtiTensor' in Package `dti'
dti-class

Class "dti"
print-methods

Methods for Function `print' in Package `dti'
readDWIdata

Read Diffusion Weighted Data
summary-methods

Methods for Function `summary' in Package `dti'
sdpar-methods

Methods for Function `sdpar' in Package `dti'
extract-methods

Methods for Function `extract' and `[' in Package `dti'
dti.smooth-methods

Methods for Function `dti.smooth' in Package `dti'
show3d-methods

Methods for Function `show3d' in Package `dti'
dti-package

DTI Analysis
medinria

Read/Write Diffusion Tensor Data from/to NIFTI File
dtiIndices-methods

Methods for Function `dtiIndices' in Package `dti'
show-methods

Methods for Function `show' in Package `dti'