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

Description

The method estimates, in each voxel, a mixture of radial symmetric tensors from the DWI data contained in an object of class "dtiData".

Usage

# S4 method for dtiData
dwiMixtensor(object, maxcomp=3,
          model=c("MT","MTiso","MTisoFA","MTisoEV"), fa=NULL,
          lambda=NULL, mask=NULL, reltol=1e-10, maxit=5000, ngc=1000,
          nguess=100*maxcomp^2, msc=c("BIC","AIC","AICC","none"),
          mc.cores = setCores(,reprt=FALSE))
# S4 method for dwiMixtensor,dwiMixtensor
dwiMtCombine(mtobj1, mtobj2, msc="BIC", where=NULL)

Value

An object of class "dwiMixtensor".

Arguments

object: Object of class "dtiData"
maxcomp: Maximal number of mixture components.
model: Specifies the mixture model used. "MT" corresponds to a mixture of prolate tensors, "MTiso" includes an isotropic compartment, "MTisoFA" additionally fixes FA to the value given in argument fa and "MTisoEV" uses eigenvalues specified by fa and lambda.
fa: Value for FA in case of model="MTisoFA" or model="MTisoEV"
lambda: Value for first eigenvalue in case of model="MTisoEV"
mask: Brain mask
reltol: Relative tolerance for R's optim() function.
maxit: Maximal number of iterations in R's optim() function.
ngc: provide information on number of voxel processed, elapsed time and estimated remaining time after ngc voxel.
nguess: number of guesses in search for initial estimates
msc: Criterion used to select the order of the mixture model, either BIC (Bayes Information Criterion) AIC (Akaike Information Criterion) or AICC ((Bias-)Corrected Akaike Information Criterion). None may be specified to only correct for under-estimation of variances.
mtobj1: For method "dwiMtCombine" an "dwiMixtensor"-object.
where: Mask of voxel for which "dwiMtImprove" or "dwiMtCombine" should be performed.
mtobj2: For method "dwiMtCombine" an "dwiMixtensor"-object obtained from the same "dwiData" object. The maximum number of components in mtobj2 should preferably be less or equal to the maximum number of components in mtobj1.
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.

Author

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

Details

For model=="MT" the function estimates, in each voxel, a mixture of radial symmetric (prolate) tensors from the DWI data contained in an object of class "dtiData". The number of mixture components is selected depending on the data, with a maximum number of components specified by maxcomp. Optimization is performed usin R's internal BFGS code with mixture weights (volumes of compartments corresponding to a tensor component) computed using the Lawson-Hannson NNLS code. model=="MT" is only available for single shell data. In case of model=="MTiso" the model additionally contains an isotropic compartment. Optimization uses the internal L-BFGS-B code. model=="MTisoFA" and model=="MTisoEV" fix FA and eigenvalues of the prolate tensors, respectively, in the tensor mixture model with isotropic compartment.

The method "dwiMtCombine" enables to combine results obtained for the same dwi data set with different specifications, e.g. for maximum number of components mcomp and settings that influence initial estimates. The combined result contains in each voxel the best result from both reconstructions with respect to the specified model selection criterion msc.

References

Jian et al. (2007), A novel tensor distribution model for the diffusion-weighted MR signal, NeuroImage 37, 164--176.

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.

Examples

Run this code

  if (FALSE) demo(mixtens_art)

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