These methods estimate, in each voxel, the diffusion kurtosis tensor (and the diffusion tensor) and some scalar indices.
# S4 method for dtiData
dkiTensor(object, method=c("CLLS-QP", "CLLS-H", "ULLS", "QL", "NLR"),
sigma=NULL, L=1, mask=NULL,
mc.cores=setCores(, reprt=FALSE), verbose=FALSE)
# S4 method for dkiTensor
dkiIndices(object, mc.cores=setCores(, reprt=FALSE),
verbose=FALSE)
An object of class "dkiTensor"
or "dkiIndices"
.
Object of class "dtiData"
Method for tensor estimation. May be "CLLS-QP"
for a qudratic
programm solution for the constrained optimization (requires package quadprog),
"CLLS-H"
for a heuristic approximation described in Tabesh et al. (2011),
or "ULLS"
for an unconstrained linear least squares estimation. "QL"
and
"NLR"
correspond to the use of unconstrained quasi-likelihood and nonlinear regression,
respectively.
Scale parameter of intensity distribution (unprocessed). Used with method="QL"
in the calculation of the expected intensity values.
Effective number of coils, 2*L are the degrees of freedom of the intensity
distribution (unprocessed). The default corresponds, e.g., to the case of a SENSE reconstruction.
Used with method="QL"
in the calculation of the expected intensity values.
argument to specify a precomputed brain mask
Number of cores to use. Defaults to number of threads specified for openMP, see documentation of package awsMethods. Not yet fully implemented for these methods.
Verbose mode.
signature(object = "ANY")
Returns a warning
signature(object = "dtiData")
The method "dkiTensor"
estimates the diffusion kurtosis
model, i.e., the kurtosis tensor and the diffusion tensor.
signature(object = "dkiTensor")
The method "dkiIndices"
estimates
some scalar indices from the kurtosis tensor. The method is still experimental, some
quantities may be removed in future versions, other might be included.
Karsten Tabelow tabelow@wias-berlin.de
A. Tabesh, J.H. Jensen, B.A. Ardekani, and J.A. Helpern, Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging, Magnetic Resonance in Medicine, 65, 823-836 (2011).
E.S. Hui, M.M. Cheung, L. Qi, and E.X. Wu, Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis, Neuroimage, 42, 122-134 (2008).
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.
dtiData
,
readDWIdata
,
dtiData
,
dkiTensor
dkiIndices