dti.smooth {dti}R Documentation

Smoothing of DWI data (Diffusion tensor model)

Description

The function provides structural adaptive smoothing for diffusion weighted image data within the context of an diffusion tensor (DTI) model. It implements smoothing of DWI data using a structural assumption of a local (anisotropic) homogeneous diffusion tensor model (in case an dtiData-object is provided). It also implements adaptive smoothing of a diffusion tensor using a Rimannian metric (in case an dtiTensor-object is given), althoug we strictly recommend to use the first variant due to methodological reasons.

Usage

dti.smooth(object, ...)

Arguments

object either an object of class dtiData or an object of class dtiTensor
... additional parameters
hmax
Maximal bandwidth
hinit
Initial bandwidth (default 1)
lambda
Critical parameter (default 20)
rho
Regularization parameter for anisotropic vicinities (default 1)
graph
Logical: Visualize intermediate results (default FALSE)
slice
slice number, determines the slice used in visualization
quant
determines minanindex as corresponding quantile of FA if is.null(minanindex)
minanindex
minimal anisotropy index to use in visualization
hsig
bandwidth for presmoothing of variance estimates
lseq
sequence of correction factors for lambda
method
if model=="linear" estimates are obtained using a linearization of the tensor model. This was the estimate used in Tabelow et.al. (2008). model=="nonlinear" uses a nonlinear regression model with reparametrization that ensures the tensor to be positive semidefinite, see Koay et.al. (2006).
varmethod
specifies the method for estimating the error variance. 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.
varmodel
if varmodel=="global" a homogeneous variance estimate is assumed and estimated as the median of the local variance estimates.
rician
logical: apply a correction for Rician bias. This is still experimental and depends on spatial independence of errors.
niter
Maximum number of iterations for tensor estimates using the nonlinear model.
volseq
If volseq==TRUE the sum of location weights is fixed to 1.25^k within iteration k (does not depend on the actual tensor). Otherwise the ellipsoid of positive location weights is determined by a bandwidth h_k = 1.25^(k/3).

Details

Effective parameters depend on the class of the supplied object. We highly recommend to use function dti.smooth on DWI data directly, i.e. on an object of class dtiData, due to methodological reasons.

Value

An object of class dtiTensor.

Author(s)

Karsten Tabelow tabelow@wias-berlin.de, J"org Polzehl polzehl@wias-berlin.de

References

K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny. 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.

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

See Also

dtiData, dtiTensor, tensor2medinria , dtiData, dtiIndices, dtiTensor

Examples

## Not run: demo(dti_art)

[Package dti version 0.5-4 Index]