dtiTensor-class {dti}R Documentation

Class "dtiTensor"

Description

Diffusion Tensor object. Containes the results of estimating a diffusion tensor from diffusion weighted image (DWI) data.

Objects from the Class

Objects can be created by calls to functions dtiTensor, dti.smooth or medinria2tensor.

Slots

.Data:
Object of class "list" with optional components
s2rician
estimated variance parameter in the rice distribution (if rician=TRUE in function dti.smooth
ni
voxelwise sum of weights in adaptive smoothing (from function dti.smooth)
D:
Object of class "array" contains estimated tensors, dimension c(6,ddim). Tensors are stored as upper diagonal matrices.
th0:
Object of class "array" contains estimated intensities in S0 images, dimension ddim
sigma:
Object of class "array" containing estimated error variances
scorr:
Object of class "numeric" containing estimated spatial correlations in coordinate directions
bw:
Object of class "numeric" containing bandwidth for a Gaussian kernel that approximately creates the estimated spatial correlations. Needed for adjustments of critical values in the adaptive smoothing algorithm used in function dti.smooth
mask:
Object of class "array", logical array indicating the voxel where the tensor was estimated.
level:
Object of class "numeric" minimal valid s0-level. No evaluation was be performed for voxel with s0-values less than level. Used to determine mask.
hmax:
Object of class "numeric" maximal bandwidth in case of adaptive smoothing. contains 1 otherwise.
method:
Object of class "character" either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors.
btb:
Object of class "matrix" matrix of dimension c(6,ngrad) obtained from gradient directions.
ngrad:
Object of class "integer" number of gradients (including zero gradients)
s0ind:
Object of class "integer" index of zero gradients within sequence 1:ngrad
replind:
Object of class "integer" index (identifier) of unique gradient directions. Used to charactreize replications in the gradient design by identical indices. length is ngrad
ddim:
Object of class "integer" dimension of original image cubes. Integer vector of length 3.
ddim0:
Object of class "integer" dimension of subcube defined by xind, yind and zind.
xind:
Object of class "integer" index for subcube definition in x-direction
yind:
Object of class "integer" index for subcube definition in y-direction
zind:
Object of class "integer" index for subcube definition in z-direction
voxelext:
Object of class "numeric" voxel extensions in x-, y- and z-direction. vector of length 3.
source:
Object of class "character" name of the imgfile used to create the data.

Extends

Class "list", from data part. Class "dti", directly. Class "vector", by class "list", distance 2.

Methods

dtiIndices
signature(object = "dtiTensor"): Calculate fractal anisotropy (FA) and main directions of anisotropy from diffusion tensors.
dti.smooth
signature(object = "dtiTensor"): Smooth diffusion tensor. For exploration only. We strictly recommend using function dti.smooth on a dtiData-object.
plot
signature(x = "dtiTensor"): not jet implemented.

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).

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

See Also

dtiData,dtiTensor,dtiIndices,dti.smooth, dti, dtiData, dtiIndices

Examples

showClass("dtiTensor")
## Not run: demo(dti_art)

[Package dti version 0.5-4 Index]