sph.odfvmf: Fibre Orientation Estimation Based on von Mises Distributions...

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

View source: R/sph.odfvmf.R


Fibre orientations in multiple fibre voxels are estimated using a mixture of von Mises-Fisher (vMF) distributions. This statistical estimation procedure is used to resolve crossing fibre mappings.


sph.odfvmf(run=TRUE, fbase=NULL, savedir=tempdir(), rg=NULL, swap=FALSE,
 btoption=2, threshold=0.4, showglyph=FALSE, bview="coronal", order=4,
 clusterthr=0.6, aniso=NULL, ...)



logical variable enabling loading previously processed data (default: TRUE).


Directory where the required input data files are located.


directory for saving/loading processed results (default: tempdir().


range of slices to process; default option rg=NULL processes all slices.


toggle radiological/neurological orientation (default: FALSE).


b-table selection between ‘btable.txt’ (btoption=1), and the 3D-DSI grid b-table extracted from the diffusion data set (‘data.bvec’ and ‘data.bval’). By default, the 3D-DSI grid b-table is used (btoption=2).


thresholding generalized fractional anisotropy (GFA) value at each voxel (default: 0.4).


MRI slice view selection in {axial, coronal, sagittal} (default: "coronal").


logical variable controlling visualization of voxel glyphs (default: FALSE).


parameter associated with the order of the spherical harmonics approximation (default: 4).


thresholding orientations based on ODF values at each voxel for directional clustering (default: 0.6).


anisotropic parameter in the range "[0,1)" or NULL to use in ODF pos-processing default: NULL.


optional specification of non-default control parameters as detailed in movMF.


For directional clustering estimation sph.odfvmf uses a mixture of 2 and 4 von Mises-Fisher (vMF) distributions that serves as a model for directional ODF profile data, corresponding to multiple fibre orientations. Statistical orientation estimation in sph.odfvmf is based on von Mises clustering procedures provided by the R-package movMF, by Kurt Hornik and Bettina Gruen.

Starting with the raw diffusion signal acquired on a grid of q-space, the ODF profile is estimated at each voxel, considering a sampling density of unit vectors on a unit S2 grid. When a threshold is applied to the estimated ODF at each voxel, the non-thresholded unit vectors provide directional statistics information about the estimated ODF profile. The main ODF orientations at each voxel relevant for fibre tracking may be estimated by clustering the non-thresholded unit vectors. The Q-ball reconstruction method with Aganj regularization as implemented in dti (Tabelow and Polzehl) is used for orientation distribution function (ODF) reconstruction.

The main diffusion data set used in the examples is a DICOM data set provided by the "Advanced Biomedical MRI Lab, National Taiwan University Hospital", which is included in the "DSI Studio" package, publicly available from the NITRC repository (http://www.nitrc.org). One QBI-compatible b-table defining the acquisition setup on a S2-shell is included in the package. The b-table for the shell acquisition used in For comparison with GQI, the b-table ‘btable.txt’ has been used in the examples. This b-table has 203 points distributed on a S2-shell.

The output files ‘data_V1_sph.nii.gz’, ‘data_V2_sph.nii.gz’ and ‘data_gfa_sph.nii.gz’ are used to visualize RGB maps through rgbvolmap() or via the "FSL/fslview" tool. These files may be used to perform white matter fibre tractography.


sph.odfvmf outputs three data files in NIfTI format named
data_V1_sph.nii.gz’, ‘data_V2_sph.nii.gz’, and ‘data_gfa_sph.nii.gz’. The first and second main fibre directions per voxel are contained in ‘data_V1_sph.nii.gz’, ‘data_V2_sph.nii.gz’, respectively. The file ‘data_gfa_sph.nii.gz’ contains the GFA metric per voxel.


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


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.

Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. FSL. NeuroImage 62, 2 (2012), 782-790.

Tuch D. S., Q-Ball Imaging, Magnetic Resonance in Medicine 52 (2004), 1358-1372.

Tabelow K., Polzehl J.: dti: DTI/DWI Analysis, 2012. R package version 1.1-0.

See Also

sph.odfvmflines, sph.odfpeaklines, gqi.odfvxgrid, rgbvolmap, sph.odfpeaks, s2tessel.zorder, plotglyph, simulglyph.vmf, simul.fandtasia, simul.simplefield, data, data.bval, data.bvec, btable


## Not run: 
    ## Generate ODF volumes (QBI volume processing)
    ## for a range of slices using von Mises-Fisher clustering
    sph.odfvmf(showglyph=FALSE, threshold=0.5, savedir=tempdir())
    ## RGB maps for range of slices processed by sph.odfvmf()
    rgbvolmap(fbase=tempdir(), rg=c(1,4), bview="coronal")
    ## Show reconstructed glyphs in ODF processing 
    ## for first and second main fibre direction determination
    sph.odfvmf(rg=c(1,1), bview="coronal", showglyph=TRUE, threshold=0.5)

## End(Not run)

gdimap documentation built on May 19, 2017, 9:36 p.m.

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