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# interfaces.camino.convert

## AnalyzeHeader

Link to code

Wraps command analyzeheader

Create or read an Analyze 7.5 header file.

Analyze image header, provides support for the most common header fields. Some fields, such as patient_id, are not currently supported. The program allows three nonstandard options: the field image_dimension.funused1 is the image scale. The intensity of each pixel in the associated .img file is (image value from file) * scale. Also, the origin of the Talairach coordinates (midline of the anterior commisure) are encoded in the field data_history.originator. These changes are included for compatibility with SPM.

All headers written with this program are big endian by default.

### Example

    >>> import nipype.interfaces.camino as cmon
    >>> hdr = cmon.AnalyzeHeader()
    >>> hdr.inputs.in_file = 'tensor_fitted_data.Bfloat'
    >>> hdr.inputs.scheme_file = 'A.scheme'
    >>> hdr.inputs.data_dims = [256,256,256]
    >>> hdr.inputs.voxel_dims = [1,1,1]
    >>> hdr.run()

Inputs:

    [Mandatory]
    datatype: ('byte' or 'char' or '[u]short' or '[u]int' or 'float' or 'complex' or
             'double')
            The char datatype is 8 bit (not the 16 bit char of Java), as specified by the Analyze
            7.5 standard.      The byte, ushort and uint types are not part of the Analyze
            specification but are supported by SPM.
    in_file: (an existing file name)
            Tensor-fitted data filename

    [Optional]
    args: (a string)
            Additional parameters to the command
    centre: (a list of from 3 to 3 items which are an integer)
            Voxel specifying origin of Talairach coordinate system for SPM, default         [0 0 0].
    data_dims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    description: (a string)
            Short description - No spaces, max length 79 bytes. Will be null terminated
            automatically.
    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
    greylevels: (a list of from 2 to 2 items which are an integer)
            Minimum and maximum greylevels. Stored as shorts in the header.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    intelbyteorder: (a boolean)
            Write header in intel byte order (little-endian).
    networkbyteorder: (a boolean)
            Write header in network byte order (big-endian). This is the default for new headers.
    nimages: (an integer)
            Number of images in the img file. Default 1.
    offset: (an integer)
            According to the Analyze 7.5 standard, this is the byte offset in the .img fileat which
            voxels start. This value can be negative to specify that the absolute value isapplied
            for every image in the file.
    out_file: (a file name)
    picoseed: (a list of from 3 to 3 items which are an integer)
            Voxel specifying the seed (for PICo maps), default [0 0 0].
    scaleinter: (a float)
            Constant to add to the image intensities. Used by SPM and MRIcro.
    scaleslope: (a float)
            Intensities in the image are scaled by this factor by SPM and MRICro. Default is 1.0.
    voxel_dims: (a list of from 3 to 3 items which are a float)
            voxel dimensions in mm

Outputs:

    header: (an existing file name)
            Analyze header

## DT2NIfTI

Link to code

Wraps command dt2nii

Converts camino tensor data to NIfTI format

Reads Camino diffusion tensors, and converts them to NIFTI format as three .nii files.

Inputs:

    [Mandatory]
    header_file: (an existing file name)
             A Nifti .nii or .hdr file containing the header information
    in_file: (an existing file name)
            tract file

    [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
    output_root: (a file name)
            filename root prepended onto the names of three output files.

Outputs:

    dt: (an existing file name)
            diffusion tensors in NIfTI format
    exitcode: (an existing file name)
            exit codes from Camino reconstruction in NIfTI format
    lns0: (an existing file name)
            estimated lns0 from Camino reconstruction in NIfTI format

## Image2Voxel

Link to code

Wraps command image2voxel

Converts Analyze / NIFTI / MHA files to voxel order.

Converts scanner-order data in a supported image format to voxel-order data. Either takes a 4D file (all measurements in single image) or a list of 3D images.

### Examples

    >>> import nipype.interfaces.camino as cmon
    >>> img2vox = cmon.Image2Voxel()
    >>> img2vox.inputs.in_file = '4d_dwi.nii'
    >>> img2vox.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            4d image file

    [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
    out_file: (a file name)
    out_type: ('float' or 'char' or 'short' or 'int' or 'long' or 'double', nipype default
             value: float)
            "i.e. Bfloat". Can be "char", "short", "int", "long", "float" or "double"

Outputs:

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

## NIfTIDT2Camino

Link to code

Wraps command niftidt2camino

Converts NIFTI-1 diffusion tensors to Camino format. The program reads the NIFTI header but does not apply any spatial transformations to the data. The NIFTI intensity scaling parameters are applied.

The output is the tensors in Camino voxel ordering: [exit, ln(S0), dxx, dxy, dxz, dyy, dyz, dzz].

The exit code is set to 0 unless a background mask is supplied, in which case the code is 0 in brain voxels and -1 in background voxels.

The value of ln(S0) in the output is taken from a file if one is supplied, otherwise it is set to 0.

NOTE FOR FSL USERS - FSL’s dtifit can output NIFTI tensors, but they are not stored in the usual way (which is using NIFTI_INTENT_SYMMATRIX). FSL’s tensors follow the ITK / VTK “upper-triangular” convention, so you will need to use the -uppertriangular option to convert these correctly.

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            A NIFTI-1 dataset containing diffusion tensors. The tensors are assumed to be in lower-
            triangular order as specified by the NIFTI standard for the storage of symmetric
            matrices. This file should be either a .nii or a .hdr file.

    [Optional]
    args: (a string)
            Additional parameters to the command
    bgmask: (an existing file name)
            Binary valued brain / background segmentation, may be a raw binary file (specify type
            with -maskdatatype) or a supported image file.
    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
    lns0_file: (an existing file name)
            File containing the log of the unweighted signal for each voxel, may be a raw binary
            file (specify type with -inputdatatype) or a supported image file.
    out_file: (a file name)
    s0_file: (an existing file name)
            File containing the unweighted signal for each voxel, may be a raw binary file (specify
            type with -inputdatatype) or a supported image file.
    scaleinter: (a float)
            A value v in the diffusion tensor is scaled to v * s + i. This is applied after any
            scaling specified by the input image. Default is 0.0.
    scaleslope: (a float)
            A value v in the diffusion tensor is scaled to v * s + i. This is applied after any
            scaling specified by the input image. Default is 1.0.
    uppertriangular: (a boolean)
            Specifies input in upper-triangular (VTK style) order.

Outputs:

    out_file: (a file name)
            diffusion tensors data in Camino format

## ProcStreamlines

Link to code

Wraps command procstreamlines

Process streamline data

This program does post-processing of streamline output from track. It can either output streamlines or connection probability maps.
  - http://web4.cs.ucl.ac.uk/research/medic/camino/pmwiki/pmwiki.php?n=Man.procstreamlines

### Examples

    >>> import nipype.interfaces.camino as cmon
    >>> proc = cmon.ProcStreamlines()
    >>> proc.inputs.in_file = 'tract_data.Bfloat'
    >>> proc.inputs.outputtracts = 'oogl'
    >>> proc.run()

Inputs:

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

    [Optional]
    allowmultitargets: (a boolean)
            Allows streamlines to connect to multiple target volumes.
    args: (a string)
            Additional parameters to the command
    datadims: (a list of from 3 to 3 items which are an integer)
            data dimensions in voxels
    directional: (a list of from 3 to 3 items which are an integer)
            Splits the streamlines at the seed point and computes separate connection probabilities
            for each segment. Streamline segments are grouped according to their dot product with
            the vector (X, Y, Z). The ideal vector will be tangential to the streamline trajectory
            at the seed, such that the streamline projects from the seed along (X, Y, Z) and -(X, Y,
            Z). However, it is only necessary for the streamline trajectory to not be orthogonal to
            (X, Y, Z).
    discardloops: (a boolean)
            This option allows streamlines to enter a waypoint exactly once. After the streamline
            leaves the waypoint, the entire streamline is discarded upon a second entry to the
            waypoint.
    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
    inputmodel: ('raw' or 'voxels', nipype default value: raw)
            input model type (raw or voxels)
    iterations: (a float)
            Number of streamlines generated for each seed. Not required when outputting streamlines,
            but needed to create PICo images. The default is 1 if the output is streamlines, and
            5000 if the output is connection probability images.
    maxtractlength: (an integer)
            maximum length of tracts
    maxtractpoints: (an integer)
            maximum number of tract points
    mintractlength: (an integer)
            minimum length of tracts
    mintractpoints: (an integer)
            minimum number of tract points
    noresample: (a boolean)
            Disables resampling of input streamlines. Resampling is automatically disabled if the
            input model is voxels.
    out_file: (a file name)
    outputacm: (a boolean)
            output all tracts in a single connection probability map (Analyze image)
    outputcbs: (a boolean)
            outputs connectivity-based segmentation maps; requires target outputfile
    outputcp: (a boolean)
            output the connection probability map (Analyze image, float)
    outputsc: (a boolean)
            output the connection probability map (raw streamlines, int)
    outputtracts: ('raw' or 'voxels' or 'oogl', nipype default value: raw)
            output tract file type
    regionindex: (an integer)
            index of specific region to process
    resamplestepsize: (a float)
            Each point on a streamline is tested for entry into target, exclusion or waypoint
            volumes. If the length between points on a tract is not much smaller than the voxel
            length, then streamlines may pass through part of a voxel without being counted. To
            avoid this, the program resamples streamlines such that the step size is one tenth of
            the smallest voxel dimension in the image. This increases the size of raw or oogl
            streamline output and incurs some performance penalty. The resample resolution can be
            controlled with this option or disabled altogether by passing a negative step size or by
            passing the -noresample option.
    seedpointmm: (a list of from 3 to 3 items which are an integer)
            The coordinates of a single seed point for tractography in mm
    seedpointvox: (a list of from 3 to 3 items which are an integer)
            The coordinates of a single seed point for tractography in voxels
    truncateinexclusion: (a boolean)
            Retain segments of a streamline before entry to an exclusion ROI.
    truncateloops: (a boolean)
            This option allows streamlines to enter a waypoint exactly once. After the streamline
            leaves the waypoint, it is truncated upon a second entry to the waypoint.
    voxeldims: (a list of from 3 to 3 items which are an integer)
            voxel dimensions in mm

Outputs:

    proc: (an existing file name)
            Processed Streamlines

## TractShredder

Link to code

Wraps command tractshredder

Extracts bunches of streamlines.

tractshredder works in a similar way to shredder, but processes streamlines instead of scalar data. The input is raw streamlines, in the format produced by track or procstreamlines.

The program first makes an initial offset of offset tracts. It then reads and outputs a group of bunchsize tracts, skips space tracts, and repeats until there is no more input.

### Examples

    >>> import nipype.interfaces.camino as cmon
    >>> shred = cmon.TractShredder()
    >>> shred.inputs.in_file = 'tract_data.Bfloat'
    >>> shred.inputs.offset = 0
    >>> shred.inputs.bunchsize = 1
    >>> shred.inputs.space = 2
    >>> shred.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            tract file

    [Optional]
    args: (a string)
            Additional parameters to the command
    bunchsize: (an integer)
            reads and outputs a group of bunchsize tracts
    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
    offset: (an integer)
            initial offset of offset tracts
    out_file: (a file name)
    space: (an integer)
            skips space tracts

Outputs:

    shredded: (an existing file name)
            Shredded tract file

## VtkStreamlines

Link to code

Wraps command vtkstreamlines

Use vtkstreamlines to convert raw or voxel format streamlines to VTK polydata

### Examples

    >>> import nipype.interfaces.camino as cmon
    >>> vtk = cmon.VtkStreamlines()
    >>> vtk.inputs.in_file = 'tract_data.Bfloat'
    >>> vtk.inputs.voxeldims = [1,1,1]
    >>> vtk.run()

Inputs:

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

    [Optional]
    args: (a string)
            Additional parameters to the command
    colourorient: (a boolean)
            Each point on the streamline is coloured by the local orientation.
    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: ('raw' or 'voxels', nipype default value: raw)
            input model type (raw or voxels)
    interpolate: (a boolean)
            the scalar value at each point on the streamline is calculated by trilinear
            interpolation
    interpolatescalars: (a boolean)
            the scalar value at each point on the streamline is calculated by trilinear
            interpolation
    out_file: (a file name)
    voxeldims: (a list of from 3 to 3 items which are an integer)
            voxel dimensions in mm

Outputs:

    vtk: (an existing file name)
            Streamlines in VTK format