dti (version 0.5-4)

dti.smooth: Smoothing of DWI data (Diffusion tensor model)

Description

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.

Usage

dti.smooth(object, ...)

Arguments

object

either an object of class dtiData or an object of class dtiTensor

...

additional parameters hmax{Maximal bandwidth} hinit{Initial bandwidth (default 1)} lambda{Critical parameter (default 20)} rho{Regularization parameter for anisotropic vicinities (default 1)} graph

Value

• An object of class dtiTensor.

Details

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.

References

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/

See Also

dtiData, dtiTensor, tensor2medinria , dtiData, dtiIndices, dtiTensor

Examples

demo(dti_art)