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# interfaces.camino.calib

## SFLUTGen

Link to code

Wraps command sflutgen

Generates PICo lookup tables (LUT) for multi-fibre methods such as PASMRI and Q-Ball.

SFLUTGen creates the lookup tables for the generalized multi-fibre implementation of the PICo tractography algorithm. The outputs of this utility are either surface or line coefficients up to a given order. The calibration can be performed for different distributions, such as the Bingham and Watson distributions.

This utility uses calibration data generated from SFPICOCalibData and peak information created by SFPeaks.

The utility outputs two lut’s, *_oneFibreSurfaceCoeffs.Bdouble and *_twoFibreSurfaceCoeffs.Bdouble. Each of these files contains big- endian doubles as standard. The format of the output is:

    dimensions    (1 for Watson, 2 for Bingham)
    order         (the order of the polynomial)
    coefficient_1
    coefficient_2
    ~~~
    coefficient_N

In the case of the Watson, there is a single set of coefficients, which are ordered:

    constant, x, x^2, ..., x^order.

In the case of the Bingham, there are two sets of coefficients (one for each surface), ordered so that:

    for j = 1 to order
      for k = 1 to order
        coeff_i = x^j * y^k
    where j+k < order

### Example

To create a calibration dataset using the default settings

    >>> import nipype.interfaces.camino as cam
    >>> lutgen = cam.SFLUTGen()
    >>> lutgen.inputs.in_file = 'QSH_peaks.Bdouble'
    >>> lutgen.inputs.info_file = 'PICO_calib.info'
    >>> lutgen.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Voxel-order data of the spherical functions peaks.
            flag: -inputfile %s
    info_file: (a file name)
            The Info file that corresponds to the calibration datafile used in
            the reconstruction.
            flag: -infofile %s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    binincsize: (an integer)
            Sets the size of the bins. In the case of 2D histograms such as the
            Bingham, the bins are always square. Default is 1.
            flag: -binincsize %d
    directmap: (a boolean)
            Use direct mapping between the eigenvalues and the distribution
            parameters instead of the log of the eigenvalues.
            flag: -directmap
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    minvectsperbin: (an integer)
            Specifies the minimum number of fibre-orientation estimates a bin
            must contain before it is used in the lut line/surface generation.
            Default is 50. If you get the error "no fibre-orientation estimates
            in histogram!", the calibration data set is too small to get enough
            samples in any of the histogram bins. You can decrease the minimum
            number per bin to get things running in quick tests, but the sta-
            tistics will not be reliable and for serious applications, you need
            to increase the size of the calibration data set until the error
            goes.
            flag: -minvectsperbin %d
    order: (an integer)
            The order of the polynomial fitting the surface. Order 1 is linear.
            Order 2 (default) is quadratic.
            flag: -order %d
    out_file: (a file name)
            flag: > %s, position: -1
    outputstem: (a string, nipype default value: LUT)
            Define the name of the generated luts. The form of the filenames
            will be [outputstem]_oneFibreSurfaceCoeffs.Bdouble and
            [outputstem]_twoFibreSurfaceCoeffs.Bdouble
            flag: -outputstem %s
    pdf: ('bingham' or 'watson', nipype default value: bingham)
            Sets the distribution to use for the calibration. The default is the
            Bingham distribution, which allows elliptical probability density
            contours. Currently supported options are: bingham - The Bingham
            distribution, which allows elliptical probability density contours.
            watson - The Watson distribution. This distribution is rotationally
            symmetric.
            flag: -pdf %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    lut_one_fibre: (an existing file name)
            PICo lut for one-fibre model
    lut_two_fibres: (an existing file name)
            PICo lut for two-fibre model

## SFPICOCalibData

Link to code

Wraps command sfpicocalibdata

Generates Spherical Function PICo Calibration Data.

SFPICOCalibData creates synthetic data for use with SFLUTGen. The synthetic data is generated using a mixture of gaussians, in the same way datasynth generates data. Each voxel of data models a slightly different fibre configuration (varying FA and fibre- crossings) and undergoes a random rotation to help account for any directional bias in the chosen acquisition scheme. A second file, which stores information about the datafile, is generated along with the datafile.

### Example 1

To create a calibration dataset using the default settings

    >>> import nipype.interfaces.camino as cam
    >>> calib = cam.SFPICOCalibData()
    >>> calib.inputs.scheme_file = 'A.scheme'
    >>> calib.inputs.snr = 20
    >>> calib.inputs.info_file = 'PICO_calib.info'
    >>> calib.run()

The default settings create a large dataset (249,231 voxels), of which 3401 voxels contain a single fibre population per voxel and the rest of the voxels contain two fibre-populations. The amount of data produced can be varied by specifying the ranges and steps of the parameters for both the one and two fibre datasets used.

### Example 2

To create a custom calibration dataset

    >>> import nipype.interfaces.camino as cam
    >>> calib = cam.SFPICOCalibData()
    >>> calib.inputs.scheme_file = 'A.scheme'
    >>> calib.inputs.snr = 20
    >>> calib.inputs.info_file = 'PICO_calib.info'
    >>> calib.inputs.twodtfarange = [0.3, 0.9]
    >>> calib.inputs.twodtfastep = 0.02
    >>> calib.inputs.twodtanglerange = [0, 0.785]
    >>> calib.inputs.twodtanglestep = 0.03925
    >>> calib.inputs.twodtmixmax = 0.8
    >>> calib.inputs.twodtmixstep = 0.1
    >>> calib.run()

This would provide 76,313 voxels of synthetic data, where 3401 voxels simulate the one fibre cases and 72,912 voxels simulate the various two fibre cases. However, care should be taken to ensure that enough data is generated for calculating the LUT. # doctest: +SKIP

Inputs:

    [Mandatory]
    info_file: (a file name)
            The name to be given to the information output filename.
            flag: -infooutputfile %s
    scheme_file: (an existing file name)
            Specifies the scheme file for the diffusion MRI data
            flag: -schemefile %s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    onedtfarange: (a list of from 2 to 2 items which are a float)
            Minimum and maximum FA for the single tensor synthetic data.
            flag: -onedtfarange %s
    onedtfastep: (a float)
            FA step size controlling how many steps there are between the
            minimum and maximum FA settings.
            flag: -onedtfastep %f
    out_file: (a file name)
            flag: > %s, position: -1
    seed: (a float)
            Specifies the random seed to use for noise generation in simulation
            trials.
            flag: -seed %f
    snr: (a float)
            Specifies the signal-to-noise ratio of the non-diffusion-weighted
            measurements to use in simulations.
            flag: -snr %f
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    trace: (a float)
            Trace of the diffusion tensor(s) used in the test function.
            flag: -trace %f
    twodtanglerange: (a list of from 2 to 2 items which are a float)
            Minimum and maximum crossing angles between the two fibres.
            flag: -twodtanglerange %s
    twodtanglestep: (a float)
            Angle step size controlling how many steps there are between the
            minimum and maximum crossing angles for the two tensor cases.
            flag: -twodtanglestep %f
    twodtfarange: (a list of from 2 to 2 items which are a float)
            Minimum and maximum FA for the two tensor synthetic data. FA is
            varied for both tensors to give all the different permutations.
            flag: -twodtfarange %s
    twodtfastep: (a float)
            FA step size controlling how many steps there are between the
            minimum and maximum FA settings for the two tensor cases.
            flag: -twodtfastep %f
    twodtmixmax: (a float)
            Mixing parameter controlling the proportion of one fibre population
            to the other. The minimum mixing parameter is (1 - twodtmixmax).
            flag: -twodtmixmax %f
    twodtmixstep: (a float)
            Mixing parameter step size for the two tensor cases. Specify how
            many mixing parameter increments to use.
            flag: -twodtmixstep %f

Outputs:

    PICOCalib: (an existing file name)
            Calibration dataset
    calib_info: (an existing file name)
            Calibration dataset