brain2d: Two-dimensional Diffusion Weighted Dataset

In svcm: 2d and 3d Space-Varying Coefficient Models

Description

The data set consists of six transformed diffusion weighted images (DWI) showing a representative axial slice of the human brain. The stored values can directly be passed to estimate the diffusion tensor elements (regression coefficients) using a transform of the applied gradients as regressors.

Usage

data(brain2d)

Format

The first two dimensions provide the transformed signal intensities of one brain slice sized 90 x 75 voxels. The third dimension encodes for the direction of the six applied diffusion weighting gradients.

Details

The present DTI data set was acquired at 1.5 T (Signa Echospeed; GE Medical Systems) using a spin-echo echo-planar sequence with TR/TE = 4200ms/120ms and diffusion gradients in a six noncollinear directions with a b-value of 880 s/mm^2. One axial slice was selected from a volume of six DWI (b = 880 s/mm^2) and one reference image (b = 0 s/mm^2). In-plane resolution amounts to 1.875 x 1.875 mm^2.

The transformation of the raw signal intensities,

y = - 1/b log(S_i/S_0)

is derived from the Stejskal-Tanner equation and is proposed by Papadakis et al.

Source

Diffusion Tensor Imaging was performed at the Max-Planck-Institute of Psychiatry, Munich, Germany.

References

Basser P. J. and Jones D. K. (2002) Diffusion-tensor MRI: Theory, experimental design and data analysis – a technical review. NMR in Biomedicine 15, 456-467.

Mori S. and Barker P. B. (1999) Diffusion magnetic resonance imaging: Its principle and applications. The Anatomical Record 257, 102-109.

Papadakis N. G., Xing D., Huang C. L.-H., Hall L. and Carpenter T. A. (1999). A comparative study of acquisition schemes for diffusion tensor imaging using MRI. Journal of Magnetic Resonance 137, 67-82.

Stejskal E. O. and Tanner J. E. (1965) Spin diffusion measurements: Spin echoes in the presence of time-dependent field gradient. The Journal of Chemical Physics 42, 288-292.

Examples

data(brain2d)
dim(brain2d)
old.par <- par(no.readonly=TRUE)
par(pin=c(6, 1.2))
image(t(matrix(brain2d, dim(brain2d)[1], dim(brain2d)[2]*6)), axes=FALSE,
      col=grey.colors(256))
title("Six Diffusion Weighted Images")
par(old.par)  

Example output

Loading required package: Matrix
Loading required package: splines
[1] 90 75  6

svcm documentation built on May 2, 2019, 1:29 p.m.