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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

**-luts**<*Files containing LUTs*>-
Files containing the lookup tables. For tensor data, one lut must be specified for each
type of inversion used in the image (one-tensor, two-tensor, three-tensor). For pds, the
number of LUTs must match -numpds (it is acceptable to use the same LUT several times -
see example, above). These LUTs may be generated with
**dtlutgen**. **-inputmodel**<*dt*|*multitensor*|*pds*>-
The type of model used for the input.
**-numpds**<*number*>-
The maximum number of PDs in a voxel (default 3) for PD data. This option determines the
size of the input and output voxels. This means that the data file may be large enough to
accomodate three or more PDs, but does not mean that any of the voxels are classified as
containing three or more PDs.
**-pdf**<*watson*|*bingham*|*acg*>-
Specifies the PDF to use. There are three choices:
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/

**-directmap**-
Only applicable when using pds as the inputmodel. Use direct mapping between the
eigenvalues and the distribution parameters instead of the log of the eigenvalues.
**-maxcomponents**<*number*>-
The maximum number of tensor components in a voxel (default 2) for multitensor data.
Currently, only the default is supported, but future releases may allow the input of
three-tensor data using this option.

Kiran K Seunarine <camino@cs.ucl.ac.uk>

- NAME
- SYNOPSIS
- DESCRIPTION
- EXAMPLES
- OPTIONS
- AUTHORS
- SEE ALSO
- BUGS

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Time: 02:07:11 GMT, December 04, 2017