dti.smooth-methods {dti}R Documentation

Methods for Function ‘dti.smooth’ in Package ‘dti’

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 a "dtiData"-object is provided). It also implements structural adaptive smoothing of a diffusion tensor using a Riemannian metric (in case a "dtiTensor"-object is given), although we strictly recommend to use the first variant due to methodological reasons.

Usage

  ## S4 method for signature 'dtiData':
  dti.smooth(object, hmax=5, hinit=NULL, lambda=20, rho=1, graph=FALSE, slice=NULL, quant=.8, minanindex=NULL, hsig=2.5, lseq=NULL, method="nonlinear", varmethod="residuals", rician=TRUE, niter=5, varmodel="local")
 

Arguments

object Either an object of class "dtiData" or an object of class "dtiTensor"
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 Method for tensor estimation. May be "linear", or "nonlinear".
varmethod Specifies the method for estimating the error variance. May be varmethod=="replicates", or "residuals".
varmodel Specifies the model for the variance. May be "global", or "local".
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.

Value

An object of class dtiTensor.

Methods

object = "ANY"
Returns a warning.
object = "dtiData"
We highly recommend to use the method dti.smooth on DWI data directly, i.e. on an object of class "dtiData", due to methodological reasons, see Tabelow et al. (2008). It is usually not necessary to use any other argument than hmax, which defines the maximum bandwidth of the iteration.

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). If varmethod=="replicates" the error variance is estimated from replicated gradient directions if possible, otherwise (default) an estimate is obtained from the residual sum of squares. 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).

Author(s)

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

References

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

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

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

See Also

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


[Package dti version 0.6-0 Index]