Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 |
gdi |
method of ODF reconstruction to use |
run |
logical variable enabling loading previously processed data (default: |
fbase |
Directory where the required input data files are located. |
savedir |
directory for saving/loading processed results (default: |
rg |
range of slices to process; default option |
swap |
toggle radiological/neurological orientation (default: |
lambda |
diffusion sampling length in |
depth |
sampling density on the hemisphere used in simulation (default N=321; depth=3). |
btoption |
b-table selection between ‘btable.txt’ ( |
threshold |
thresholding generalized fractional anisotropy (GFA) value at each voxel (default: 0.4). |
bview |
MRI slice view selection in { |
showglyph |
logical variable controlling visualization of voxel glyphs (default: |
clusterthr |
thresholding orientations based on ODF values at each voxel for directional clustering (default: 0.6). |
aniso |
anisotropic parameter in the range "[0,1)" or |
... |
optional specification of non-default control parameters as detailed in |
GQI methods specify an operational sampling scheme in q-space from which the ODF can be estimated.
GQI (Yeh et.al. 2010) or GQI2 (Garyfallidis 2012) may be used for ODF reconstruction.
For directional clustering estimation gqi.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 gqi.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 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). Two b-tables defining the acquisition setup are specified. One is a b-table for a S2-like grid denoted by ‘btable.txt’. The other is the b-table for the 3D-DSI sampling scheme used in the DICOM data acquisition. This b-table has 203 points uniformly distributed on a 3D grid limited to the volume of the unit sphere. In both tables, the b-values range from 0 to 4000. Sampling densities of N=81 (depth=2) and N=321 (depth=3) on the hemisphere are often used in ODF profile reconstruction from diffusion acquisitions.
The output files ‘data_V1_gqi.nii.gz’, ‘data_V2_gqi.nii.gz’, ‘data_V3_gqi.nii.gz’, and ‘data_gfa_gqi.nii.gz’ may be used for probabilistic white matter tractography. These principal diffusion direction (PDD) files retain information about the 'theta' and 'alpha' parameters of the von Mises-Fisher mixture at each voxel. The file ‘data_V123_gqi.nii.gz’ joins all three PDD files in a single NIfTI file. For visualization purposes via a external tool such as "FSL/fslview" the voxel PDDs must be normalized to the unit sphere beforehand by using niinorm
.
gqi.odfvmf
outputs three data files in NIfTI format named
‘data_V1_gqi.nii.gz’, ‘data_V2_gqi.nii.gz’, and
‘data_gfa_gqi.nii.gz’.
The first and second main fibre directions per voxel are contained in ‘data_V1_gqi.nii.gz’, ‘data_V2_gqi.nii.gz’, respectively. The file ‘data_gfa_gqi.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.
Yeh, F.-C., Wedeen, V. J., and Tseng, W.-Y. I. Generalized q-Sampling Imaging. IEEE Transactions on Medical Imaging 29, 9 (2010), 1626-1635.
Garyfallidis E., Towards an Accurate Brain Tractography, 2012, PhD Thesis, University of Cambridge.
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. FSL. NeuroImage 62, 2 (2012), 782-790.
gqi.odfvmflines
,
gqi.odfpeaklines
,
gqi.odfvxgrid
,
rgbvolmap
,
gqi.odfpeaks
,
s2tessel.zorder
,
plotglyph
,
simulglyph.vmf
,
simul.fandtasia
,
simul.simplefield
,
data
,
data.bval
,
data.bvec
,
btable
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
## Generate ODF volumes (GQI volume processing)
## for a range of slices using von Mises-Fisher clustering
gqi.odfvmf(depth=2, showglyph=FALSE, threshold=0.5, savedir=tempdir())
## RGB maps for range of slices processed by gqi.odfvmf()
rgbvolmap(fbase=tempdir(), rg=c(1,4), bview="coronal")
##-------------
## Show reconstructed glyphs in ODF processing
## for first and second main fibre direction determination
gqi.odfvmf(gdi="gqi", rg=c(1,1), bview="coronal", depth=3,
showglyph=TRUE, threshold=0.5)
gqi.odfvmf(gdi="gqi2", rg=c(1,1), bview="coronal", depth=3,
showglyph=TRUE, threshold=0.5)
##-------------
## speeded up approximations: hardmax and numeric kappa
gqi.odfvmf(depth=2, showglyph=FALSE, threshold=0.5, savedir=tempdir(),
E="hardmax", kappa=20)
rgbvolmap(fbase=tempdir(), rg=c(1,4), bview="coronal")
## End(Not run)
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