dti (version 1.5.4.3)

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

Description

The method estimates, in each voxel, the diffusion tensor from the DWI data contained in an object of class "dtiData".

Usage

# S4 method for dtiData
dtiTensor(object, method=c( "nonlinear", "linear", "quasi-likelihood"),
          sigma = NULL, L = 1, mask=NULL, mc.cores = setCores( , reprt = FALSE))

Value

An object of class "dtiTensor".

Arguments

object

Object of class "dtiData"

method

Method for tensor estimation. May be "linear", or "nonlinear". method=="quasi-likelihood" solves the nonlinear regression problem with the expected value of the signal as regression function and weighting according to the signal variance.

sigma

(local) scale parameter of the signal's distribution.

L

(local) effective degrees of freedom.

mask

argument to specify a precomputed brain mask

mc.cores

Number of cores to use. Defaults to number of threads specified for openMP, see documentation of package awsMethods. Our experience suggests to use 4-6 cores if available.

Methods

obj = "ANY"

Returns a warning.

obj = "dtiData"

Estimate diffusion tensor from data in each voxel with the different options for the regression type and model for variance estimation. If method=="linear" estimates are obtained using a linearization of the tensor model. This was the estimate used in Tabelow et.al. (2008). method=="nonlinear" uses a nonlinear regression model with reparametrization that ensures the tensor to be positive semidefinite, see Koay et.al. (2006). The imlementation is based on R's internal C code for the BFGS optimization. method=="quasi-likelihood" solves the nonlinear regression problem with the expected value of the signal as regression function and weighting according to the signal variance. Tis requires additional parameters sigma and L characterizing the distribution of the signal. If varmethod=="replicates" the error variance is estimated from replicated gradient directions if possible, otherwise an estimate is obtained from the residual sum of squares. If varmodel=="global" a homogeneous variance is assumed and estimated as the median of the local variance estimates. sigma and 2*L are the scale parameter and degrees of freedom of the (local) signal distribution. L characterizes the effective number of coils. Both parameters are either scalars or arrays of the size of the images.

Author

Karsten Tabelow
J\"org Polzehl

References

J. Polzehl and K. Tabelow, Beyond the diffusion tensor model: The package dti, Journal of Statistical Software, 44(12), 1-26 (2011).

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(1), 200-211 (2012).

J. Polzehl and K. Tabelow, Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software, 31(9) 1-24 (2009).

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

C.G. Koay, J.D. Carew, A.L. Alexander, P.J. Basser and M.E. Meyerand. Investigation of Anomalous Estimates of Tensor-Derived Quantities in Diffusion Tensor Imaging, Magnetic Resonance in Medicine, 2006, 55, 930-936.

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.

https://www.wias-berlin.de/projects/matheon_a3/

See Also

dtiData, readDWIdata, dtiIndices-methods, medinria, dtiData, dtiTensor dwiMixtensor

Examples

  if (FALSE) demo(dti_art)