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# interfaces.camino.dti

## ComputeEigensystem

Link to code

Wraps command dteig

Computes the eigensystem from tensor fitted data.

Reads diffusion tensor (single, two-tensor, three-tensor or multitensor) data from the standard input, computes the eigenvalues and eigenvectors of each tensor and outputs the results to the standard output. For multiple-tensor data the program outputs the eigensystem of each tensor. For each tensor the program outputs: {l_1, e_11, e_12, e_13, l_2, e_21, e_22, e_33, l_3, e_31, e_32, e_33}, where l_1 >= l_2 >= l_3 and e_i = (e_i1, e_i2, e_i3) is the eigenvector with eigenvalue l_i. For three-tensor data, for example, the output contains thirty-six values per voxel.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> dteig = cmon.ComputeEigensystem()
    >>> dteig.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> dteig.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Tensor-fitted data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    inputdatatype: ('double' or 'char' or 'short' or 'int' or 'long' or 'float')
            Specifies the data type of the input file. The data type can be any of the following
            strings: "char", "short", "int", "long", "float" or "double".
    inputmodel: ('dt' or 'multitensor')
            Specifies the model that the input data contains parameters for. Possible model types
            are: "dt" (diffusion-tensor data) and "multitensor"
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel of the input data.
    out_file: (a file name)
    outputdatatype: ('double' or 'char' or 'short' or 'int' or 'long' or 'float')
            Specifies the data type of the output data. The data type can be any of the following
            strings: "char", "short", "int", "long", "float" or "double".

Outputs:

    eigen: (an existing file name)
            Trace of the diffusion tensor

## ComputeFractionalAnisotropy

Link to code

Wraps command fa

Computes the fractional anisotropy of tensors.

Reads diffusion tensor (single, two-tensor or three-tensor) data from the standard input, computes the fractional anisotropy (FA) of each tensor and outputs the results to the standard output. For multiple-tensor data the program outputs the FA of each tensor, so for three-tensor data, for example, the output contains three fractional anisotropy values per voxel.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> fa = cmon.ComputeFractionalAnisotropy()
    >>> fa.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> fa.inputs.scheme_file = 'A.scheme'
    >>> fa.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Tensor-fitted data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    inputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the input file. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".
    inputmodel: ('dt' or 'twotensor' or 'threetensor' or 'multitensor')
            Specifies the model that the input tensor data contains parameters for.Possible model
            types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), "threetensor"
            (three-tensor data). By default, the program assumes that the input data contains a
            single diffusion tensor in each voxel.
    out_file: (a file name)
    outputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the output data. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".

Outputs:

    fa: (an existing file name)
            Fractional Anisotropy Map

## ComputeMeanDiffusivity

Link to code

Wraps command md

Computes the mean diffusivity (trace/3) from diffusion tensors.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> md = cmon.ComputeMeanDiffusivity()
    >>> md.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> md.inputs.scheme_file = 'A.scheme'
    >>> md.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Tensor-fitted data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    inputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the input file. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".
    inputmodel: ('dt' or 'twotensor' or 'threetensor')
            Specifies the model that the input tensor data contains parameters for.Possible model
            types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), "threetensor"
            (three-tensor data). By default, the program assumes that the input data contains a
            single diffusion tensor in each voxel.
    out_file: (a file name)
    outputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the output data. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".

Outputs:

    md: (an existing file name)
            Mean Diffusivity Map

## ComputeTensorTrace

Link to code

Wraps command trd

Computes the trace of tensors.

Reads diffusion tensor (single, two-tensor or three-tensor) data from the standard input, computes the trace of each tensor, i.e., three times the mean diffusivity, and outputs the results to the standard output. For multiple-tensor data the program outputs the trace of each tensor, so for three-tensor data, for example, the output contains three values per voxel.

Divide the output by three to get the mean diffusivity.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> trace = cmon.ComputeTensorTrace()
    >>> trace.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> trace.inputs.scheme_file = 'A.scheme'
    >>> trace.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Tensor-fitted data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    inputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the input file. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".
    inputmodel: ('dt' or 'twotensor' or 'threetensor' or 'multitensor')
            Specifies the model that the input tensor data contains parameters for.Possible model
            types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), "threetensor"
            (three-tensor data). By default, the program assumes that the input data contains a
            single diffusion tensor in each voxel.
    out_file: (a file name)
    outputdatatype: ('char' or 'short' or 'int' or 'long' or 'float' or 'double')
            Specifies the data type of the output data. The data type can be any of thefollowing
            strings: "char", "short", "int", "long", "float" or "double".

Outputs:

    trace: (an existing file name)
            Trace of the diffusion tensor

## DTIFit

Link to code

Wraps command dtfit

Reads diffusion MRI data, acquired using the acquisition scheme detailed in the scheme file, from the data file.

Use non-linear fitting instead of the default linear regression to the log measurements. The data file stores the diffusion MRI data in voxel order with the measurements stored in big-endian format and ordered as in the scheme file. The default input data type is four-byte float. The default output data type is eight-byte double. See modelfit and camino for the format of the data file and scheme file. The program fits the diffusion tensor to each voxel and outputs the results, in voxel order and as big-endian eight-byte doubles, to the standard output. The program outputs eight values in each voxel: [exit code, ln(S(0)), D_xx, D_xy, D_xz, D_yy, D_yz, D_zz]. An exit code of zero indicates no problems. For a list of other exit codes, see modelfit(1). The entry S(0) is an estimate of the signal at q=0.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> fit = cmon.DTIFit()
    >>> fit.inputs.scheme_file = 'A.scheme'
    >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> fit.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            voxel-order data filename
    scheme_file: (an existing file name)
            Camino scheme file (b values / vectors, see camino.fsl2scheme)

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    non_linear: (a boolean)
            Use non-linear fitting instead of the default linear regression to the log measurements.
    out_file: (a file name)

Outputs:

    tensor_fitted: (an existing file name)
            path/name of 4D volume in voxel order

## DTLUTGen

Link to code

Wraps command dtlutgen

Calibrates the PDFs for PICo probabilistic tractography.

This program needs to be run once for every acquisition scheme. It outputs a lookup table that is used by the dtpicoparams program to find PICo PDF parameters for an image. The default single tensor LUT contains parameters of the Bingham distribution and is generated by supplying a scheme file and an estimated signal to noise in white matter regions of the (q=0) image. The default inversion is linear (inversion index 1).

Advanced users can control several options, including the extent and resolution of the LUT, the inversion index, and the type of PDF. See dtlutgen(1) for details.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> dtl = cmon.DTLUTGen()
    >>> dtl.inputs.snr = 16
    >>> dtl.inputs.scheme_file = 'A.scheme'
    >>> dtl.run()

Inputs:

    [Mandatory]
    scheme_file: (a file name)
            The scheme file of the images to be processed using this LUT.

    [Optional]
    acg: (a boolean)
            Compute a LUT for the ACG PDF.
    args: (a string)
            Additional parameters to the command
    bingham: (a boolean)
            Compute a LUT for the Bingham PDF. This is the default.
    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
    frange: (a list of from 2 to 2 items which are a float)
            Index to two-tensor LUTs. This is the fractional anisotropy         of the two tensors.
            The default is 0.3 to 0.94
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inversion: (an integer)
            Index of the inversion to use. The default is 1 (linear single tensor inversion).
    lrange: (a list of from 2 to 2 items which are a float)
            Index to one-tensor LUTs. This is the ratio L1/L3 and L2 / L3.The LUT is square, with
            half the values calculated (because L2 / L3 cannot be less than L1 / L3 by
            definition).The minimum must be >= 1. For comparison, a ratio L1 / L3 = 10 with L2 / L3
            = 1 corresponds to an FA of 0.891, and L1 / L3 = 15 with L2 / L3 = 1 corresponds to an
            FA of 0.929. The default range is 1 to 10.
    out_file: (a file name)
    samples: (an integer)
            The number of synthetic measurements to generate at each point in the LUT. The default
            is 2000.
    snr: (a float)
            The signal to noise ratio of the unweighted (q = 0) measurements.This should match the
            SNR (in white matter) of the images that the LUTs are used with.
    step: (a float)
            Distance between points in the LUT.For example, if lrange is 1 to 10 and the step is
            0.1, LUT entries will be computed at L1 / L3 = 1, 1.1, 1.2 ... 10.0 and at L2 / L3 =
            1.0, 1.1 ... L1 / L3.For single tensor LUTs, the default step is 0.2, for two-tensor
            LUTs it is 0.02.
    trace: (a float)
            Trace of the diffusion tensor(s) used in the test function in the LUT generation. The
            default is 2100E-12 m^2 s^-1.
    watson: (a boolean)
            Compute a LUT for the Watson PDF.

Outputs:

    dtLUT: (an existing file name)
            Lookup Table

## ModelFit

Link to code

Wraps command modelfit

Fits models of the spin-displacement density to diffusion MRI measurements.

This is an interface to various model fitting routines for diffusion MRI data that fit models of the spin-displacement density function. In particular, it will fit the diffusion tensor to a set of measurements as well as various other models including two or three-tensor models. The program can read input data from a file or can generate synthetic data using various test functions for testing and simulations.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> fit = cmon.ModelFit()
    >>> fit.model = 'dt'
    >>> fit.inputs.scheme_file = 'A.scheme'
    >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> fit.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            voxel-order data filename
    model: ('dt' or 'restore' or 'algdt' or 'nldt_pos' or 'nldt' or 'ldt_wtd' or 'adc' or
             'ball_stick' or 'cylcyl dt' or 'cylcyl restore' or 'cylcyl algdt' or 'cylcyl nldt_pos'
             or 'cylcyl nldt' or 'cylcyl ldt_wtd' or 'cylcyl adc' or 'cylcyl ball_stick' or
             'cylcyl_eq dt' or 'cylcyl_eq restore' or 'cylcyl_eq algdt' or 'cylcyl_eq nldt_pos' or
             'cylcyl_eq nldt' or 'cylcyl_eq ldt_wtd' or 'cylcyl_eq adc' or 'cylcyl_eq ball_stick' or
             'pospos dt' or 'pospos restore' or 'pospos algdt' or 'pospos nldt_pos' or 'pospos nldt'
             or 'pospos ldt_wtd' or 'pospos adc' or 'pospos ball_stick' or 'pospos_eq dt' or
             'pospos_eq restore' or 'pospos_eq algdt' or 'pospos_eq nldt_pos' or 'pospos_eq nldt' or
             'pospos_eq ldt_wtd' or 'pospos_eq adc' or 'pospos_eq ball_stick' or 'poscyl dt' or
             'poscyl restore' or 'poscyl algdt' or 'poscyl nldt_pos' or 'poscyl nldt' or 'poscyl
             ldt_wtd' or 'poscyl adc' or 'poscyl ball_stick' or 'poscyl_eq dt' or 'poscyl_eq
             restore' or 'poscyl_eq algdt' or 'poscyl_eq nldt_pos' or 'poscyl_eq nldt' or 'poscyl_eq
             ldt_wtd' or 'poscyl_eq adc' or 'poscyl_eq ball_stick' or 'cylcylcyl dt' or 'cylcylcyl
             restore' or 'cylcylcyl algdt' or 'cylcylcyl nldt_pos' or 'cylcylcyl nldt' or 'cylcylcyl
             ldt_wtd' or 'cylcylcyl adc' or 'cylcylcyl ball_stick' or 'cylcylcyl_eq dt' or
             'cylcylcyl_eq restore' or 'cylcylcyl_eq algdt' or 'cylcylcyl_eq nldt_pos' or
             'cylcylcyl_eq nldt' or 'cylcylcyl_eq ldt_wtd' or 'cylcylcyl_eq adc' or 'cylcylcyl_eq
             ball_stick' or 'pospospos dt' or 'pospospos restore' or 'pospospos algdt' or 'pospospos
             nldt_pos' or 'pospospos nldt' or 'pospospos ldt_wtd' or 'pospospos adc' or 'pospospos
             ball_stick' or 'pospospos_eq dt' or 'pospospos_eq restore' or 'pospospos_eq algdt' or
             'pospospos_eq nldt_pos' or 'pospospos_eq nldt' or 'pospospos_eq ldt_wtd' or
             'pospospos_eq adc' or 'pospospos_eq ball_stick' or 'posposcyl dt' or 'posposcyl
             restore' or 'posposcyl algdt' or 'posposcyl nldt_pos' or 'posposcyl nldt' or 'posposcyl
             ldt_wtd' or 'posposcyl adc' or 'posposcyl ball_stick' or 'posposcyl_eq dt' or
             'posposcyl_eq restore' or 'posposcyl_eq algdt' or 'posposcyl_eq nldt_pos' or
             'posposcyl_eq nldt' or 'posposcyl_eq ldt_wtd' or 'posposcyl_eq adc' or 'posposcyl_eq
             ball_stick' or 'poscylcyl dt' or 'poscylcyl restore' or 'poscylcyl algdt' or 'poscylcyl
             nldt_pos' or 'poscylcyl nldt' or 'poscylcyl ldt_wtd' or 'poscylcyl adc' or 'poscylcyl
             ball_stick' or 'poscylcyl_eq dt' or 'poscylcyl_eq restore' or 'poscylcyl_eq algdt' or
             'poscylcyl_eq nldt_pos' or 'poscylcyl_eq nldt' or 'poscylcyl_eq ldt_wtd' or
             'poscylcyl_eq adc' or 'poscylcyl_eq ball_stick')
            Specifies the model to be fit to the data.
    scheme_file: (an existing file name)
            Camino scheme file (b values / vectors, see camino.fsl2scheme)

    [Optional]
    args: (a string)
            Additional parameters to the command
    bgmask: (an existing file name)
            Provides the name of a file containing a background mask computed using, for example,
            FSL's bet2 program. The mask file contains zero in background voxels and non-zero in
            foreground.
    bgthresh: (a float)
            Sets a threshold on the average q=0 measurement to separate foreground and background.
            The program does not process background voxels, but outputs the same number of values in
            background voxels and foreground voxels. Each value is zero in background voxels apart
            from the exit code which is -1.
    cfthresh: (a float)
            Sets a threshold on the average q=0 measurement to determine which voxels are CSF. This
            program does not treat CSF voxels any different to other voxels.
    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
    fixedbvalue: (a list of from 3 to 3 items which are a float)
            As above, but specifies <M> <N> <b>. The resulting scheme is the same whether you
            specify b directly or indirectly using -fixedmodq.
    fixedmodq: (a list of from 4 to 4 items which are a float)
            Specifies <M> <N> <Q> <tau> a spherical acquisition scheme with M measurements with q=0
            and N measurements with |q|=Q and diffusion time tau. The N measurements with |q|=Q have
            unique directions. The program reads in the directions from the files in directory
            PointSets.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'char' or 'short' or 'int' or 'long' or 'double')
            Specifies the data type of the input file: "char", "short", "int", "long", "float" or
            "double". The input file must have BIG-ENDIAN ordering. By default, the input type is
            "float".
    noisemap: (an existing file name)
            Specifies the name of the file to contain the estimated noise variance on the diffusion-
            weighted signal, generated by a weighted tensor fit. The data type of this file is big-
            endian double.
    out_file: (a file name)
    outlier: (an existing file name)
            Specifies the name of the file to contain the outlier map generated by the RESTORE
            algorithm.
    outputfile: (a file name)
            Filename of the output file.
    residualmap: (an existing file name)
            Specifies the name of the file to contain the weighted residual errors after computing a
            weighted linear tensor fit. One value is produced per measurement, in voxel order.The
            data type of this file is big-endian double. Images of the residuals for each
            measurement can be extracted with shredder.
    sigma: (a float)
            Specifies the standard deviation of the noise in the data. Required by the RESTORE
            algorithm.
    tau: (a float)
            Sets the diffusion time separately. This overrides the diffusion time specified in a
            scheme file or by a scheme index for both the acquisition scheme and in the data
            synthesis.

Outputs:

    fitted_data: (an existing file name)
            output file of 4D volume in voxel order

## PicoPDFs

Link to code

Wraps command picopdfs

Constructs a spherical PDF in each voxel for probabilistic tractography.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> pdf = cmon.PicoPDFs()
    >>> pdf.inputs.inputmodel = 'dt'
    >>> pdf.inputs.luts = 'lut_file'
    >>> pdf.inputs.in_file = 'voxel-order_data.Bfloat'
    >>> pdf.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            voxel-order data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    directmap: (a boolean)
            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.
    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
    inputmodel: ('dt' or 'multitensor' or 'pds', nipype default value: dt)
            input model type
    maxcomponents: (an integer)
            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.
    numpds: (an integer)
            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.
    out_file: (a file name)
    pdf: ('watson' or 'bingham' or 'acg', nipype default value: watson)
             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

Outputs:

    pdfs: (an existing file name)
            path/name of 4D volume in voxel order

## Track

Link to code

Wraps command track

Performs tractography using one of the following models: dt’, ‘multitensor’, ‘pds’, ‘pico’, ‘bootstrap’, ‘ballstick’, ‘bayesdirac’

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.Track()
    >>> track.inputs.inputmodel = 'dt'
    >>> track.inputs.in_file = 'data.Bfloat'
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    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
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts

## TrackBallStick

Link to code

Wraps command track

Performs streamline tractography using ball-stick fitted data

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.TrackBallStick()
    >>> track.inputs.in_file = 'ballstickfit_data.Bfloat'
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    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
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts

## TrackBayesDirac

Link to code

Wraps command track

Performs streamline tractography using a Bayesian tracking with Dirac priors

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.TrackBayesDirac()
    >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.inputs.scheme_file = 'bvecs.scheme'
    >>> track.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file
    scheme_file: (a file name)
            The scheme file corresponding to the data being processed.

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    curvepriorg: (a float)
            Concentration parameter for the prior distribution on fibre orientations given the fibre
            orientation at the previous step. Larger values of g make curvature less likely.
    curvepriork: (a float)
            Concentration parameter for the prior distribution on fibre orientations given the fibre
            orientation at the previous step. Larger values of k make curvature less likely.
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    datamodel: ('cylsymmdt' or 'ballstick')
            Model of the data for Bayesian tracking. The default model is "cylsymmdt", a diffusion
            tensor with cylindrical symmetry about e_1, ie L1 >= L_2 = L_3. The other model is
            "ballstick", the partial volume model (see ballstickfit).
    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
    extpriordatatype: ('float' or 'double')
            Datatype of the prior image. The default is "double".
    extpriorfile: (an existing file name)
            Path to a PICo image produced by picopdfs. The PDF in each voxel is used as a prior for
            the fibre orientation in Bayesian tracking. The prior image must be in the same space as
            the diffusion data.
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    iterations: (an integer)
            Number of streamlines to generate at each seed point. The default is 5000.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    pdf: ('bingham' or 'watson' or 'acg')
            Specifies the model for PICo priors (not the curvature priors). The default is
            "bingham".
    pointset: (an integer)
            Index to the point set to use for Bayesian likelihood calculation. The index specifies a
            set of evenly distributed points on the unit sphere, where each point x defines two
            possible step directions (x or -x) for the streamline path. A larger number indexes a
            larger point set, which gives higher angular resolution at the expense of computation
            time. The default is index 1, which gives 1922 points, index 0 gives 1082 points, index
            2 gives 3002 points.
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts

## TrackBootstrap

Link to code

Wraps command track

Performs bootstrap streamline tractography using mulitple scans of the same subject

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.TrackBootstrap()
    >>> track.inputs.scheme_file = 'bvecs.scheme'
    >>> track.inputs.bsdatafiles = ['fitted_data1.Bfloat', 'fitted_data2.Bfloat']
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.run()

Inputs:

    [Mandatory]
    bsdatafiles: (a list of items which are a file name)
            Specifies files containing raw data for repetition bootstrapping. Use -inputfile for
            wild bootstrap data.
    in_file: (an existing file name)
            input data file
    scheme_file: (a file name)
            The scheme file corresponding to the data being processed.

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    bgmask: (an existing file name)
            Provides the name of a file containing a background mask computed using, for example,
            FSL's bet2 program. The mask file contains zero in background voxels and non-zero in
            foreground.
    bsmodel: ('dt' or 'multitensor')
            Model to fit to bootstrap data. This is used for repetition bootstrapping. May be "dt"
            (default) or "multitensor". This option may be omitted if -inversion is specified.
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    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
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    inversion: (an integer)
            Tensor reconstruction algorithm for repetition bootstrapping. Default is 1 (linear
            reconstruction, single tensor).
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    iterations: (an integer)
            Number of streamlines to generate at each seed point.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm
    wildbsmodel: ('dt')
            The model to fit to the data, for wild bootstrapping. The same model is used to generate
            the the wild bootstrap data. Must be "dt", which is the default.

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts

## TrackDT

Link to code

Wraps command track

Performs streamline tractography using tensor data

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.TrackDT()
    >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    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
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts

## TrackPICo

Link to code

Wraps command track

Performs streamline tractography using the Probabilistic Index of Connectivity (PICo) algorithm

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> track = cmon.TrackPICo()
    >>> track.inputs.in_file = 'pdfs.Bfloat'
    >>> track.inputs.seed_file = 'seed_mask.nii'
    >>> track.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file

    [Optional]
    anisfile: (an existing file name)
            File containing the anisotropy map. This is required to apply an anisotropy threshold
            with non tensor data. If the map issupplied it is always used, even in tensor data.
    anisthresh: (a float)
            Terminate fibres that enter a voxel with lower anisotropy than the threshold.
    args: (a string)
            Additional parameters to the command
    curvethresh: (a float)
            Curvature threshold for tracking, expressed as the maximum angle (in degrees) between
            between two streamline orientations calculated over the length of a voxel. If the angle
            is greater than this, then the streamline terminates.
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    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
    gzip: (a boolean)
            save the output image in gzip format
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    inputdatatype: ('float' or 'double')
            input file type
    inputmodel: ('dt' or 'multitensor' or 'pds' or 'pico' or 'bootstrap' or 'ballstick' or
             'bayesdirac', nipype default value: dt)
            input model type
    ipthresh: (a float)
            Curvature threshold for tracking, expressed as the minimum dot product between two
            streamline orientations calculated over the length of a voxel. If the dot product
            between the previous and current directions is less than this threshold, then the
            streamline terminates. The default setting will terminate fibres that curve by more than
            80 degrees. Set this to -1.0 to disable curvature checking completely.
    iterations: (an integer)
            Number of streamlines to generate at each seed point. The default is 5000.
    maxcomponents: (an integer)
            The maximum number of tensor components in a voxel. This determines the size of the
            input file and does not say anything about the voxel classification. The default is 2 if
            the input model is multitensor and 1 if the input model is dt.
    numpds: (an integer)
            The maximum number of PDs in a voxel. The default is 1 for input model pico. This option
            determines the size of the voxels in the input file and does not affect tracking.
    out_file: (a file name)
            output data file
    outputtracts: ('float' or 'double' or 'oogl')
            output tract file type
    pdf: ('bingham' or 'watson' or 'acg')
            Specifies the model for PICo parameters. The default is "bingham.
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    tracked: (an existing file name)
            output file containing reconstructed tracts