Methods for Function `dwiMixtensor' in Package `dti'

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

# 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)

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.

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.

Value

An object of class "dwiMixtensor".

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.

Author

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

Examples

  if (FALSE) demo(mixtens_art)