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) lambda model=="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)