sph.odfpeaklines produces line-maps of ODF profiles for diffusion data slices using
a regularized spheric harmonics method for ODF reconstruction.
sph.odfpeaklines(run=TRUE, fbase=NULL, roi=NULL, rg=c(1,1), btoption=2, swap=FALSE, threshold=0.4, kdir=2, zfactor=5, showglyph=FALSE, showimage="linesgfa", bview="coronal", savedir=tempdir(), bg="white", order=4, texture=NULL, ...)TRUE). roi=NULL) uses an all brain mask
for the supplied data set. rg=c(1,1)); rg=NULL processes all slices. 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). FALSE). FALSE). "linesgfa").
Alternative options are:
c("none", "gfa", "lines", "linesgfa", "linesrgbmap", "linesdata")
(see Details). axial, coronal, sagittal} (default: "coronal"). tempdir()). "white") NULL - no texture). rgl.material, or specification of non-default control parameters as detailed in movMF. sph.odfpeaklines produces line-maps of ODF profiles for diffusion data slices.
The line-maps may be overlayed with generalized fractional anisotropy (GFA) relief maps, diffusion data maps or ROI maps.
The file ‘V1list.RData’ containing the first main orientation directions for all processed voxels is output for further posterior orientation processing.
sph.odfpeaklines implements the standard method of fibre orientation detection.
Local maxima of the reconstructed ODF are located simply by selecting a large number of sampled points on the sphere and searching within a fixed radius neighbourhood.
For a single main fibre orientation the method performs well.
However, for crossing fibres and other complex fibre configurations the peaks of the ODF profiles identified by the methods do not necessarily match the orientations of the distinct fibre populations.
A more robust method is implemented in sph.odfvmflines.Starting with the raw high angular resolution diffusion signal acquired on a S2-shell of q-space, the ODF profile is reconstructed at each voxel, considering a sampling density of unit vectors on a unit S2 shell. Q-ball imaging (QBI) is used for orientation distribution function (ODF) reconstruction. 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.
Slice map display and overlay selection is controlled by specifying one the arguments
c("none", "gfa", "lines", "linesgfa", "linesrgbmap", "linesdata") for showimages.
Meanings are as follows:
"none" - no visualization;
"gfa" - GFA map only;
"lines" - line map only;
"linesgfa" - GFA overlayed on line map;
"linesrgbmap" - lines overlayed on RGB map (if available);
"linesdata" - ‘data_brain.nii.gz’ is overlayed on line map.
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.
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.
sph.odfpeaks,
sph.odfvmf,
sph.odfvmflines,
gqi.odfvxgrid,
s2tessel.zorder,
plotglyph,
rgbvolmap,
simulglyph.vmf,
simul.fandtasia,
simul.simplefield,
data,
data.bval,
data.bvec,
btable
## Not run:
# ##-------------
# ## Line map using ODF peak detection
# sph.odfpeaklines(run=TRUE, showimage="lines")
# ## display line-map overlayed on GFA map
# sph.odfpeaklines(run=FALSE, showimage="linesgfa")
# ##-------------
# ## Show examples of reconstructed glyphs in ODF processing
# sph.odfpeaklines(showimage="lines", showglyph=TRUE)
# ##------------
# ## using a ROI overlay
# sph.odfpeaklines(roi="slfcst.nii.gz", showimage="linesgfa")
# ## using data overlay
# sph.odfpeaklines(showimage="linesdata")
# ## End(Not run)
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