Diffusion weighted imaging is a magnetic resonance based method to investigate
tissue micro-structure especially in the human brain via water diffusion.
Since the standard diffusion tensor model for the acquired data failes in
large portion of the brain voxel more sophisticated models have bee developed.
Here, we report on the package dti and how some of these models
can be used with the package.
The package fmri is provided for analysis of single run functional
Magnetic Resonance Imaging data. It implements structural adaptive smoothing
methods with signal detection for adaptive noise reduction which avoids blurring
of edges of activation areas. fmri provides fmri analysis from time series
modeling to signal detection and publication-ready images.
Modeling the orientation distribution function by mixtures of angular central Gaussian distributions
(2010)
In this paper we develop a tensor mixture model for diffusion weighted imaging
data using an automatic model selection criterion for the order of tensor
components in a voxel. We show that the weighted orientation distribution
function for this model can be expanded into a mixture of angular central
Gaussian distributions. We show properties of this model in extensive
simulations and in a high angular resolution experimental data set. The results
suggest that the model may improve imaging of cerebral fiber tracts. We
demonstrate how inference on canonical model parameters may give rise to new
clinical applications.
Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test
problem. Smoothing is usually used to reduce the effective number of multiple
comparisons and to locally integrate the signal and hence increase the
signal-to-noise ratio. Here, we provide a new structural adaptive segmentation
algorithm (AS)
that naturally combines the signal detection with noise reduction in one procedure.
Moreover, the new method
is closely related to a recently proposed structural adaptive smoothing
algorithm and preserves shape and spatial extent of activation areas without
blurring the borders.