| dti.smooth {dti} | R Documentation |
The function provides structural adaptive smoothing for diffusion weighted image data within the context of an diffusion tensor (DTI)
model. It implements smoothing of DWI data using a structural assumption of a local (anisotropic) homogeneous diffusion tensor model (in case an dtiData-object is provided). It also
implements adaptive smoothing of a diffusion tensor using a
Rimannian metric (in case an dtiTensor-object is given),
althoug we strictly recommend to use the first variant due to methodological reasons.
dti.smooth(object, ...)
object |
either an object of class dtiData
or an object of class dtiTensor |
... |
additional parameters
minanindex as corresponding quantile of FA if is.null(minanindex) lambdamodel=="linear" estimates are obtained using a linearization of the tensor model. This was the estimate used in Tabelow et.al. (2008). model=="nonlinear" uses a
nonlinear regression model with reparametrization that ensures the tensor to be positive semidefinite, see Koay et.al. (2006).varmethod=="replicates" the error variance is estimated from replicated
gradient directions if possible. Otherwise an estimate is obtained from the residual sum of squares.varmodel=="global" a homogeneous variance estimate is assumed and estimated as the median of the local variance estimates.volseq==TRUE the sum of location weights is fixed to 1.25^k within iteration k (does not depend on the actual tensor). Otherwise the ellipsoid of positive location weights is determined by a
bandwidth h_k = 1.25^(k/3). |
Effective parameters depend on the class of the supplied object.
We highly recommend to use function dti.smooth on
DWI data directly, i.e. on an object of class dtiData,
due to methodological reasons.
An object of class dtiTensor.
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).
C.G. Koay, J.D. Carew, A.L. Alexander, P.J. Basser and M.E. Meyerand. Investigation of Anomalous Estimates of Tensor-Derived Quantities in Diffusion Tensor Imaging, Magnetic Resonance in medicine, 2006, 55, 930-936.
http://www.wias-berlin.de/projects/matheon_a3/
dtiData, dtiTensor, tensor2medinria , dtiData, dtiIndices, dtiTensor
## Not run: demo(dti_art)