optgradients

<p>Optimal gradient directions minimizing symmetrized 
Coulomb forces on the sphere following a proposal by Jones et al.
(1999). These directions define an optimal design in DWI for given
number of gradients.</p>

datasets

Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
modality, that measures diffusion of water in tissues like the human
brain. The package contains R-functions to process diffusion-weighted
data. The functionality includes diffusion tensor imaging (DTI),
diffusion kurtosis imaging (DKI), modeling for high angular resolution
diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
tensor mixture models, several methods for structural adaptive
smoothing including POAS and msPOAS, and a streamline fiber tracking
for tensor and tensor mixture models.
The package provides functionality to manipulate and visualize results
in 2D and 3D.

Karsten Tabelow

Analysis of Diffusion Weighted Imaging (DWI) Data

Joerg Polzehl

Felix Anker

optgradients function

<p>a list with name optgrad and component ngrad-5 containing
a matrix with ngrad gradients as columns.</p>

optgradients: Optimal gradient directions for number of gradients between 6 and 162

Description

Usage

Arguments

Format