| dtiTensor-class {dti} | R Documentation |
Diffusion Tensor object. Containes the results of estimating a diffusion tensor from diffusion weighted image (DWI) data.
Objects can be created by calls to functions dtiTensor, dti.smooth or medinria2tensor.
.Data:"list" with optional components
s2ricianrician=TRUE in function dti.smoothnidti.smooth)D:"array" contains estimated tensors, dimension c(6,ddim).
Tensors are stored as upper diagonal matrices.th0:"array" contains estimated intensities in S0 images, dimension ddimsigma:"array" containing estimated error variancesscorr:"numeric" containing estimated spatial correlations in coordinate directionsbw:"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.smoothmask:"array", logical array indicating the voxel where the tensor was estimated.level:"numeric" minimal valid s0-level.
No evaluation was be performed for voxel with s0-values less than level. Used to determine mask.hmax:"numeric" maximal bandwidth in case of adaptive smoothing. contains 1 otherwise.method:"character" either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors.btb:"matrix" matrix of dimension c(6,ngrad) obtained from gradient directions.ngrad:"integer" number of gradients (including zero gradients) s0ind:"integer" index of zero gradients within sequence 1:ngrad replind:"integer" index (identifier) of unique
gradient directions. Used to charactreize replications in the gradient design by identical indices. length is ngradddim:"integer" dimension of original image cubes. Integer vector of length 3. ddim0:"integer" dimension of subcube defined by xind, yind and zind. xind:"integer" index for subcube definition in x-direction yind:"integer" index for subcube definition in y-direction zind:"integer" index for subcube definition in z-direction voxelext:"numeric" voxel extensions in x-, y- and z-direction. vector of length 3. source:"character" name of the imgfile used to create the data.
Class "list", from data part.
Class "dti", directly.
Class "vector", by class "list", distance 2.
signature(object = "dtiTensor"): Calculate fractal anisotropy (FA) and main directions of anisotropy from diffusion tensors. signature(object = "dtiTensor"): Smooth diffusion tensor. For exploration only. We strictly recommend using function dti.smooth on a dtiData-object. signature(x = "dtiTensor"): not jet implemented. Karsten Tabelow tabelow@wias-berlin.de, J"org Polzehl polzehl@wias-berlin.de
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/
dtiData,dtiTensor,dtiIndices,dti.smooth, dti, dtiData, dtiIndices
showClass("dtiTensor")
## Not run: demo(dti_art)