| dtiData-class {dti} | R Documentation |
Diffusion Weighted Image (DWI) Data
Objects can be created by calls of function dtiData.
.Data:"list" with component sic(ddim,ngrad)level:"numeric" minimal valid s0-level.
No evaluation will be performed for voxel with s0-values less than level. 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. orientation:"integer" vector of length 3. Orientation of data according to AFNI convention.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 = "dtiData"): Create estimates of diffusion tensors in each voxel using structural adaptive spatial smoothing. signature(object = "dtiData"): Create estimates of diffusion tensors in each voxel. signature(x = "dtiData"): not yet 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, dti.smooth, dti, dtiTensor, dtiIndices
showClass("dtiData")
## Not run: demo(dti_art)