synthfiberss2z: Voxel Diffusion Profiles for Multiple Fibre Simulation

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/synthfiberss2z.R

Description

synthfiberss2z simulates apparent diffusion coefficient (ADC) profiles in multi-direction, diffusion-weighted MR data, for testing ODF reconstruction and fibre orientation estimation.

Usage

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synthfiberss2z(g0, angles=c(20,100), b=3000, S0=1, sigma=NULL,
 logplot=TRUE, pos=c(0,0,0), showglyph=FALSE, new=TRUE, wi=NULL)

Arguments

g0

matrix of 3D points on the S2 shell used in simulation.

angles

angles in degrees of fibres to be used in simulation (default: two fibres with angles c(20,100)).

b

strength of the magnetic diffusion gradient (default b-value=3000).

S0

signal intensity without the diffusion weighting (default: 1).

sigma

Rician noise level used in simulation (default NULL).

logplot

logical variable for selecting log-scale (default TRUE).

pos

3D positional coordinate (default c(0,0,0)).

showglyph

logical variable controlling visualization of voxel glyph (default: TRUE).

new

starts a new figure if TRUE (default new=TRUE).

wi

weight given to fiber's volume fraction. Example for two fibers with different weights wi=c(0.7,0.3) (default NULL gives equal weigth to all fibers.)

Details

The simulation models the profile of the ADC over the sphere. Prolate diffusion tensor (DT) white matter profiles are estimated with eigenvalues {1700, 200, 200}(x 10^(-6) mm2/s) (see D.C. Alexander, 2002). Diffusion profiles for crossing fibres are simulated from prolate DTs in equal proportions, where each fibre is represented by a prolate DT. Noisy profiles may be simulated by adding Rician noise to the simulated diffusion profile, with a user defined standard deviation level specified as σ (SNR=1/σ). Typically, noise values of SNR~30 are used in simulated dMRI.

Value

synthfiberss2z plots the diffusion profile and returns the synthesized diffusion signal.

Author(s)

Adelino Ferreira da Silva, Universidade Nova de Lisboa, Faculdade de Ciencias e Tecnologia, Portugal, afs at fct.unl.pt

References

Barber, C. B., Habel, K., Grasman, R., Gramacy, R. B., Stahel, A., and Sterratt, D. C. geometry: Mesh generation and surface tessellation, 2012. R package version 0.3-2.

Adler, D., and Murdoch, D. rgl: 3D visualization device system (OpenGL), 2012. R package version 0.92.880.

Alexander, D. C., Barker, G. J., and Arridge, S. R. Detection and Modeling of Non-Gaussian Apparent Diffusion Coefficient Profiles in Human Brain Data. Magnetic Resonance in Medicine 48 (2002), 331-340.

See Also

simulglyph.vmf, plotglyph gqi.odfvmflines, rgbvolmap, gqi.odfpeaks, gqi.odfpeaklines, gqi.odfvxgrid, simulglyph.vmf, simul.fandtasia, simul.simplefield

Examples

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## Not run: 
    ## S2 grid
    s2 <- s2tessel.zorder(depth=3)
    g0 <- s2$pc
    ## synthetize diffusion signal (two crossing fibres)
    open3d()
    angles=c(20,100); b=3000
    S <- synthfiberss2z(g0=g0, angles=angles, b=b)
    ## synthetize signal with different volume fractions
    S <- synthfiberss2z(g0=g0, angles=angles, b=b, wi=c(0.7,0.3))
    ## synthesize diffusion signal (three crossing fibres)
    angles <- c(0,60,120); b <- 3000
    S <- synthfiberss2z(g0=g0, angles=angles, b=b)

## End(Not run)

gdimap documentation built on May 29, 2017, 11:03 p.m.

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