simul.fandtasia: Simulation of Curved Fibre Bundles for von Mises-Fisher Fibre...

In gdimap: Generalized Diffusion Magnetic Resonance Imaging

Description

The synthesized field of diffusion profiles generated by simul.fandtasia are used to reconstruct ODF profiles using the GQI method. ODF profiles and fibre directions are estimated by relying on von Mises-Fisher (vMF) distributions for directional mapping.

Usage

simul.fandtasia(gdi="gqi", gridsz=32, b=4000, depth=3, sigma=0.01,
 clusterthr=0.6, showglyph=FALSE, savedir=tempdir(), ...)

Arguments

gdi	method of ODF reconstruction to use c("gqi", "gqi2") (default: "gqi").
gridsz	dimension of squared grid to use in simulation (default 32)
b	strength of the magnetic diffusion gradient (default b-value=4000).
depth	sampling densities on the hemisphere used in simulation (default N=321; depth=3).
sigma	Rician noise level used in simulation; (default 0.01).
clusterthr	thresholding orientations based on ODF values at each voxel for directional clustering (default: 0.6).
showglyph	logical variable controlling visualization of voxel glyphs (default: FALSE).
savedir	directory for saving/loading processed results (default: tempdir().
...	optional specification of non-default control parameters as detailed in movMF.

Details

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). The procedure is adapted from Barmpoutis' code to generate synthetic tensor diffusion-weighted MRI fields. The procedure is very time intensive for grids of size 32x32.

Value

simul.fandtasia returns a field of 32x32 diffusion profiles in NIfTI format.

Author(s)

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

References

Ferreira da Silva, A. R. Computational Representation of White Matter Fiber Orientations, International Journal of Biomedical Imaging, Vol. 2013, Article ID 232143, Hindawi Publishing Corporation http://dx.doi.org/10.1155/2013/232143.

Ferreira da Silva, A. R. Facing the Challenge of Estimating Human Brain White Matter Pathways. In Proc. of the 4th International Joint Conference on Computational Intelligence (Oct. 2012), K. Madani, J. Kacprzyk, and J. Filipe, Eds., SciTePress, pp. 709-714.

Hornik, K., and Gruen, B. movMF: Mixtures of von Mises-Fisher Distributions, 2012. R package version 0.1-0.

Barmpoutis, A. Tutorial on Diffusion Tensor MRI using Matlab. Electronic Edition, University of Florida, 2010,
http://www.mathworks.com/matlabcentral/fileexchange/file_infos/26997-fandtasia-toolbox.

See Also

simul.fandtasiaSignal, simulglyph.vmf, simul.simplefield

Examples

## Not run: 
    ## simulation with a new generated field of profiles,
    ## of size 16x16 (for speed), with added noise 
    simul.fandtasia(gridsz=16, sigma=0.01)
    simul.fandtasia(gdi="gqi2", gridsz=16, sigma=0.01)
    ## same as before, but showing crossing-fibre glyphs 
    simul.fandtasia(gridsz=16, sigma=0.01, showglyph=TRUE)
    simul.fandtasia(gdi="gqi2", gridsz=16, sigma=0.01, showglyph=TRUE)
    ## using a 32x32 data field as in the original reference
    ## Warning: time-consuming example
    simul.fandtasia()
    ## speeded up approximations: hardmax and numeric kappa
    simul.fandtasia(gridsz=16, sigma=0.01, E="hardmax", kappa=20)

## End(Not run)

gdimap documentation built on May 2, 2019, 8:52 a.m.