"dwi" classes is used for Diffusion Weighted Imaging (DWI) data and, within the Diffusion Tensor Model (DTI), diffusion tenors and its indices. "dwi" is only a superclass, no instances should be created. However, objects can be created by calls of the form new("dwi", ...). "dtiData", "dtiTensor", and "dtiIndices" can be created from their correspondingly named functions and methods..Data: "list", usually empty. gradient: "matrix", matrix of dimension c(3,ngrad) containing gradient directions. btb: "matrix", matrix of dimension c(6,ngrad) obtained from gradient directions. bvalue: "numeric", of length ngrad containing b-values if available.ngrad: "integer", number of gradients (including zero gradients). s0ind: "integer", index of zero gradients within the sequence 1:ngrad. replind: "integer", index (identifier) of unique gradient directions. Used to characterize replications in the gradient design by identical indices. length is ngrad. ddim: "integer", dimension of subcube defined by xind, yind and zind. ddim0: "integer", dimension of original image cubes. Vector of length 3. xind, yind, zind:"integer", index for subcube definition in x-, y- and z-direction. voxelext: "numeric", voxel extensions in x-, y- and z-direction. Vector of length 3. orientation: "integer", orientation of data according to AFNI convention. Vector of length 3. rotation: "matrix", optional rotation matrix for gradient directions. level: "numeric", minimal valid S0-level. No evaluation will be performed for voxels with S0-values less than level. source: "character", name of the source imgage file or source directory. call: "call", call that created the object. "dtiData": si: "array", Diffusion Weighted Data. sdcoef: "numeric", Parameters of the model for error
standard deviation as a function of the mean. First two entries refer to intercept and slope of a linear function,
third and fourth value are the endpoints of the interval of linearity. Contains rep(0,4) if not set. If the function "dtiTensor": D: "array", estimated tensors, dimension c(6,ddim).
Tensors are stored as upper diagonal matrices.th0: "array", estimated intensities in S0 images, dimension ddimsigma: "array", estimated error variances if method=="linear", zero otherwise.scorr: "numeric", estimated spatial correlations in coordinate directionsbw: "numeric", 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 indicating the voxel where the tensor was estimated.hmax: "numeric", maximal bandwidth in case of adaptive smoothing, 1 otherwise.outlier: "numeric", index of voxel where physical constraints are not met, i.e. where the observed values in gradient images Si were larger than the corresponding S0 values. These are probably motion effects or registration errors. Values are replaced by the corresponding (mean) S0 values.scale:show3d.dtiTensormethod: "character", either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors."dtiIndices": fa: "array", Fractional anisotropy values (FA)ga: "array", Geodetic anisotropy values (GA)md: "array", Mean diffusivity values (MD) andir: "array", Main directions of anisotropy bary: "array", Shape parameters method: "character" either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors."dkiTensor": D: "array", estimated tensors, dimension c(6,ddim).
Tensors are stored as upper diagonal matrices.W: "array", estimated kurtosis tensors, dimension c(15,ddim).th0: "array", estimated intensities in S0 images, dimension ddimsigma: "array", estimated error variances if method=="linear", zero otherwise.scorr: "numeric", estimated spatial correlations in coordinate directionsbw: "numeric", 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 indicating the voxel where the tensor was estimated.hmax: "numeric", maximal bandwidth in case of adaptive smoothing, 1 otherwise.outlier: "numeric", index of voxel where physical constraints are not met, i.e. where the observed values in gradient images Si were larger than the corresponding S0 values. These are probably motion effects or registration errors. Values are replaced by the corresponding (mean) S0 values.scale:show3d.dtiTensormethod: "character", either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors."dkiIndices": fa: "array", Fractional anisotropy values (FA)ga: "array", Geodetic anisotropy values (GA)md: "array", Mean diffusivity values (MD) andir: "array", Main directions of anisotropy bary: "array", Shape parameters k1: "array", Kurtosis along DT (Hui et al. 2008) k2: "array", Kurtosis along DT (Hui et al. 2008) k3: "array", Kurtosis along DT (Hui et al. 2008) mk: "array", Mean kurtosis (Hui et al. 2008) mk2: "array", Mean Kurtosis (Tabesh et al. (2011))kaxial: "array", Axial kurtosis (Hui et al. 2008) kradial: "array", Radial kurtosis (Hui et al. 2008) fak: "array", Kurtosis anisotropy (Hui et al. 2008) method: "character" either "linear" or "nonlinear" or "unknown". Indicates the regression model used for estimating the tensors."dwiQball": order: "integer", maximal order of Spherical Harmonics to use, needs to be even.forder: "integer", maximal order Gaussian-Laguerre functions in SPF basis (for EAP estimation)zeta: "numeric", Scale parameter used in Gaussian-Laguerre functions (for EAP estimation)lambda: "numeric", nonnegative regularization parameter.sphcoef: "array", estimated coefficients for spherical harmonics, dimension c((order+1)*(order+2)/2,ddim).sigma: "array", estimated error variances if method=="linear", zero otherwise.scorr: "numeric", estimated spatial correlations in coordinate directionsbw: "numeric", 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 indicating the voxel where the tensor was estimated.hmax: "numeric", maximal bandwidth in case of adaptive smoothing, 1 otherwise.outlier: "numeric", index of voxel where physical constraints are not met, i.e. where the observed values in gradient images Si were larger than the corresponding S0 values. These are probably motion effects or registration errors. Values are replaced by the corresponding (mean) S0 values.scale:show3d.dwiQballwhat: "character", "ODF", "wODF", "aODF" or "ADC". Indicates if the object contains coefficients of the orientation density function (ODF (Descoteaux 2007), wODF (Sapiro(2009) or aODF) or the apparent diffusion coefficient (ADC). Coefficients are computed with respect to spherical harmonics of the specified order."dwiFiber": fibers: "matrix", Matrix of fibers. The first three columns contain the coordinates of the track points, the last three columns the direction vectors for each of these points.startind: "integer", indices for the first dimension of fibers where
coordinates for a new fiber start.roix: "integer", coordinate range of region of interest in x-direction roiy: "integer", coordinate range of region of interest in x-direction roiz: "integer", coordinate range of region of interest in x-direction method: "character", fiber tracking method.minfa: "numeric", minimal fractional anisotropy indexmaxangle: "numeric", maximal angle between fibres."dwiMixtensor": model: "character", characterizes the type of the
mixed tensor model. Currently the only implemented model is model="homogeneous_prolate".
ev: "array", estimated eigenvalues, dimension c(2,ddim)mix: "array", estimated mixture coefficients, dimension c(nmix,ddim). nmix is the number of mixture components specified.orient: "array", estimated tensor orientations, dimension c(2,nmix,ddim)th0: "array", estimated intensities in S0 images, dimension ddimsigma: "array", estimated error variances if method=="linear", zero otherwise.scorr: "numeric", estimated spatial correlations in coordinate directionsbw: "numeric", 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 indicating the voxel where the tensor was estimated.hmax: "numeric", maximal bandwidth in case of adaptive smoothing, 1 otherwise.outlier: "numeric", index of voxel where physical constraints are not met, i.e. where the observed values in gradient images Si were larger than the corresponding S0 values. These are probably motion effects or registration errors. Values are replaced by the corresponding (mean) S0 values.scale:show3d.dtiTensormethod: "character", either "mixtensor" or "Jian". Indicates the regression model used for estimating the tensors."dtiData", "dtiTensor", "dtiIndices", "dwiQball" and "dwiFiber".
signature(object = "dtiData"): Create estimates of diffusion tensors in each voxel. signature(object = "dtiTensor"): Create estimates of diffusion tensors indices in each voxel. signature(object = "dtiTensor") or signature(object = "dtiIndices"): Fiber tracking. signature(object = "dtiData"): Create estimates of ADC-parameters with respect to a sherical harmonics ortho-normal system. K. Tabelow, H.U. Voss and J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Journal of Neuroscience Methods, to appear.
J. Polzehl and K. Tabelow, Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software, 31 (2009) pp. 1--24. K. Tabelow, J. Polzehl, V. Spokoiny and H.U. Voss. Diffusion Tensor Imaging: Structural adaptive smoothing, NeuroImage 39(4), 1763-1773 (2008).
dtiData,
readDWIdata,
sdpar-methods,
getsdofsb-methods,
dwiRiceBias-methods,
dtiTensor-methods,
dwiMixtensor-methods,
dti.smooth-methods,
dwi.smooth-methods,
dtiIndices-methods,
dwiQball-methods,
tracking-methods,
show3d-methods,
plot-methods,
print-methods,
summary-methods,
extract-methods