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[numPDsClass, PD1_PARAMS, PD2_PARAMS, PD{max}_PARAMS]
where numPDsClass is determined from the input data. This is the number of PDs that the voxel is classified as containing, and may range from 0 to max. This parameter does not affect the length of the voxel; if the number of PDs are less than max, the extra space is filled with zeros.
The parameters for each principal direction, PD{n}_PARAMS, are
[mix, e1_x, e1_y, e1_z, e2_x, e2_y, e2_z, e3_x, e3_y, e3_z, picoParam_1...picoParam_N]
The input data is the output of either modelfit or sfpeaks. Non-tensor data can be used by generating pds from sfpeaks. Both tensor data and non-tensor data can also be used with track for deterministic streamline tractography, see sfpeaks(1) and track(1).
The parameter mix is the mixing parameter from a multi-compartment Gaussian model, or the peak strength associated with the PD by sfpeaks. The three orthogonal unit vectors e1, e2, e3 describe the orientation of the PDF. The parameters picoParam_1...N are the N concentration parameters of the PDF, taken from the lookup table.
Map parameters from a lookup table to an image.
cat SubjectA.oneDT.Bdouble | picopdfs -pdf watson -luts watsonLUT_Ascheme_snr16_inv1 -inputmodel dt > SubjectA.oneDT.watson.Bdouble
Map two-tensor data. We pass two LUTs, the first for voxels containing one tensor and the second for voxels containing two tensors.
picopdfs -inputfile SubjectA.twoEig.Bdouble -pdf bingham -luts binghamLUT_Ascheme_snr16_inv1 twoTensorBinghamLUT_Ascheme_snr16_inv1 -inputmodel multitensor > SubjectA.twoDT.bingham.Bdouble
In the next example we show how to use the calibration from sflutgen to create a PICo map using pds from sfpeaks. Note that we pass three LUTs to picopdfs. This is because we have specified -numpds 3. We therefore need to have a separate LUT for each of the 1, 2 and 3 fibre cases.
cat SubjectA.pds.Bdouble | picopdfs -inputmodel pds -numpds 3 -pdf bingham -luts bingham_oneFibreSurfaceCoeffs.Bdouble bingham_twoFibreSurfaceCoeffs.Bdouble bingham_twoFibreSurfaceCoeffs.Bdouble > SubjectA.pds_map.Bdouble
watson - The Watson distribution. This distribution is rotationally symmetric.
bingham - The Bingham distributionn, which allows elliptical probability density contours.
acg - The Angular Central Gaussian distribution, which also allows elliptical probability density contours/
Kiran K Seunarine <camino@cs.ucl.ac.uk>