interfaces.mrtrix.tracking

DiffusionTensorStreamlineTrack

Link to code

Wraps the executable command streamtrack.

Specialized interface to StreamlineTrack. This interface is used for streamline tracking from diffusion tensor data, and calls the MRtrix function ‘streamtrack’ with the option ‘DT_STREAM’

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> dtstrack = mrt.DiffusionTensorStreamlineTrack()
>>> dtstrack.inputs.in_file = 'data.Bfloat'
>>> dtstrack.inputs.seed_file = 'seed_mask.nii'
>>> dtstrack.run()                                  

Inputs:

[Mandatory]
gradient_encoding_file: (an existing file name)
        Gradient encoding, supplied as a 4xN text file with each line is in
        the format [ X Y Z b ], where [ X Y Z ] describe the direction of
        the applied gradient, and b gives the b-value in units (1000
        s/mm^2). See FSL2MRTrix
        argument: ``-grad %s``, position: -2
in_file: (an existing file name)
        the image containing the source data.The type of data required
        depends on the type of tracking as set in the preceeding argument.
        For DT methods, the base DWI are needed. For SD methods, the SH
        harmonic coefficients of the FOD are needed.
        argument: ``%s``, position: -2

[Optional]
seed_file: (an existing file name)
        seed file
        argument: ``-seed %s``
        mutually_exclusive: seed_file, seed_spec
seed_spec: (a list of from 4 to 4 items which are a float)
        seed specification in mm and radius (x y z r)
        argument: ``-seed %s``, position: 2
        mutually_exclusive: seed_file, seed_spec
include_file: (an existing file name)
        inclusion file
        argument: ``-include %s``
        mutually_exclusive: include_file, include_spec
include_spec: (a list of from 4 to 4 items which are a float)
        inclusion specification in mm and radius (x y z r)
        argument: ``-include %s``, position: 2
        mutually_exclusive: include_file, include_spec
exclude_file: (an existing file name)
        exclusion file
        argument: ``-exclude %s``
        mutually_exclusive: exclude_file, exclude_spec
exclude_spec: (a list of from 4 to 4 items which are a float)
        exclusion specification in mm and radius (x y z r)
        argument: ``-exclude %s``, position: 2
        mutually_exclusive: exclude_file, exclude_spec
mask_file: (an existing file name)
        mask file. Only tracks within mask.
        argument: ``-mask %s``
        mutually_exclusive: mask_file, mask_spec
mask_spec: (a list of from 4 to 4 items which are a float)
        Mask specification in mm and radius (x y z r). Tracks will be
        terminated when they leave the ROI.
        argument: ``-mask %s``, position: 2
        mutually_exclusive: mask_file, mask_spec
inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
          value: DT_STREAM)
        input model type
        argument: ``%s``, position: -3
stop: (a boolean)
        stop track as soon as it enters any of the include regions.
        argument: ``-stop``
do_not_precompute: (a boolean)
        Turns off precomputation of the legendre polynomial values. Warning:
        this will slow down the algorithm by a factor of approximately 4.
        argument: ``-noprecomputed``
unidirectional: (a boolean)
        Track from the seed point in one direction only (default is to track
        in both directions).
        argument: ``-unidirectional``
no_mask_interpolation: (a boolean)
        Turns off trilinear interpolation of mask images.
        argument: ``-nomaskinterp``
step_size: (a float)
        Set the step size of the algorithm in mm (default is 0.2).
        argument: ``-step %s``
minimum_radius_of_curvature: (a float)
        Set the minimum radius of curvature (default is 2 mm for DT_STREAM,
        0 for SD_STREAM, 1 mm for SD_PROB and DT_PROB)
        argument: ``-curvature %s``
desired_number_of_tracks: (an integer (int or long))
        Sets the desired number of tracks.The program will continue to
        generate tracks until this number of tracks have been selected and
        written to the output file(default is 100 for *_STREAM methods, 1000
        for *_PROB methods).
        argument: ``-number %d``
maximum_number_of_tracks: (an integer (int or long))
        Sets the maximum number of tracks to generate.The program will not
        generate more tracks than this number, even if the desired number of
        tracks hasn't yet been reached(default is 100 x number).
        argument: ``-maxnum %d``
minimum_tract_length: (a float)
        Sets the minimum length of any track in millimeters (default is 10
        mm).
        argument: ``-minlength %s``
maximum_tract_length: (a float)
        Sets the maximum length of any track in millimeters (default is 200
        mm).
        argument: ``-length %s``
cutoff_value: (a float)
        Set the FA or FOD amplitude cutoff for terminating tracks (default
        is 0.1).
        argument: ``-cutoff %s``
initial_cutoff_value: (a float)
        Sets the minimum FA or FOD amplitude for initiating tracks (default
        is twice the normal cutoff).
        argument: ``-initcutoff %s``
initial_direction: (a list of from 2 to 2 items which are an integer
          (int or long))
        Specify the initial tracking direction as a vector
        argument: ``-initdirection %s``
out_file: (a file name)
        output data file
        argument: ``%s``, position: -1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

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

FilterTracks

Link to code

Wraps the executable command filter_tracks.

Use regions-of-interest to select a subset of tracks from a given MRtrix track file.

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> filt = mrt.FilterTracks()
>>> filt.inputs.in_file = 'tracks.tck'
>>> filt.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input tracks to be filtered
        argument: ``%s``, position: -2

[Optional]
include_file: (an existing file name)
        inclusion file
        argument: ``-include %s``
        mutually_exclusive: include_file, include_spec
include_spec: (a list of from 4 to 4 items which are a float)
        inclusion specification in mm and radius (x y z r)
        argument: ``-include %s``, position: 2
        mutually_exclusive: include_file, include_spec
exclude_file: (an existing file name)
        exclusion file
        argument: ``-exclude %s``
        mutually_exclusive: exclude_file, exclude_spec
exclude_spec: (a list of from 4 to 4 items which are a float)
        exclusion specification in mm and radius (x y z r)
        argument: ``-exclude %s``, position: 2
        mutually_exclusive: exclude_file, exclude_spec
minimum_tract_length: (a float)
        Sets the minimum length of any track in millimeters (default is 10
        mm).
        argument: ``-minlength %s``
out_file: (a file name)
        Output filtered track filename
        argument: ``%s``, position: -1
no_mask_interpolation: (a boolean)
        Turns off trilinear interpolation of mask images.
        argument: ``-nomaskinterp``
invert: (a boolean)
        invert the matching process, so that tracks that wouldotherwise have
        been included are now excluded and vice-versa.
        argument: ``-invert``
quiet: (a boolean)
        Do not display information messages or progress status.
        argument: ``-quiet``, position: 1
debug: (a boolean)
        Display debugging messages.
        argument: ``-debug``, position: 1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

out_file: (an existing file name)
        the output filtered tracks

ProbabilisticSphericallyDeconvolutedStreamlineTrack

Link to code

Wraps the executable command streamtrack.

Performs probabilistic tracking using spherically deconvolved data

Specialized interface to StreamlineTrack. This interface is used for probabilistic tracking from spherically deconvolved data, and calls the MRtrix function ‘streamtrack’ with the option ‘SD_PROB’

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> sdprobtrack = mrt.ProbabilisticSphericallyDeconvolutedStreamlineTrack()
>>> sdprobtrack.inputs.in_file = 'data.Bfloat'
>>> sdprobtrack.inputs.seed_file = 'seed_mask.nii'
>>> sdprobtrack.run()                                                       

Inputs:

[Mandatory]
in_file: (an existing file name)
        the image containing the source data.The type of data required
        depends on the type of tracking as set in the preceeding argument.
        For DT methods, the base DWI are needed. For SD methods, the SH
        harmonic coefficients of the FOD are needed.
        argument: ``%s``, position: -2

[Optional]
maximum_number_of_trials: (an integer (int or long))
        Set the maximum number of sampling trials at each point (only used
        for probabilistic tracking).
        argument: ``-trials %s``
seed_file: (an existing file name)
        seed file
        argument: ``-seed %s``
        mutually_exclusive: seed_file, seed_spec
seed_spec: (a list of from 4 to 4 items which are a float)
        seed specification in mm and radius (x y z r)
        argument: ``-seed %s``, position: 2
        mutually_exclusive: seed_file, seed_spec
include_file: (an existing file name)
        inclusion file
        argument: ``-include %s``
        mutually_exclusive: include_file, include_spec
include_spec: (a list of from 4 to 4 items which are a float)
        inclusion specification in mm and radius (x y z r)
        argument: ``-include %s``, position: 2
        mutually_exclusive: include_file, include_spec
exclude_file: (an existing file name)
        exclusion file
        argument: ``-exclude %s``
        mutually_exclusive: exclude_file, exclude_spec
exclude_spec: (a list of from 4 to 4 items which are a float)
        exclusion specification in mm and radius (x y z r)
        argument: ``-exclude %s``, position: 2
        mutually_exclusive: exclude_file, exclude_spec
mask_file: (an existing file name)
        mask file. Only tracks within mask.
        argument: ``-mask %s``
        mutually_exclusive: mask_file, mask_spec
mask_spec: (a list of from 4 to 4 items which are a float)
        Mask specification in mm and radius (x y z r). Tracks will be
        terminated when they leave the ROI.
        argument: ``-mask %s``, position: 2
        mutually_exclusive: mask_file, mask_spec
inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
          value: DT_STREAM)
        input model type
        argument: ``%s``, position: -3
stop: (a boolean)
        stop track as soon as it enters any of the include regions.
        argument: ``-stop``
do_not_precompute: (a boolean)
        Turns off precomputation of the legendre polynomial values. Warning:
        this will slow down the algorithm by a factor of approximately 4.
        argument: ``-noprecomputed``
unidirectional: (a boolean)
        Track from the seed point in one direction only (default is to track
        in both directions).
        argument: ``-unidirectional``
no_mask_interpolation: (a boolean)
        Turns off trilinear interpolation of mask images.
        argument: ``-nomaskinterp``
step_size: (a float)
        Set the step size of the algorithm in mm (default is 0.2).
        argument: ``-step %s``
minimum_radius_of_curvature: (a float)
        Set the minimum radius of curvature (default is 2 mm for DT_STREAM,
        0 for SD_STREAM, 1 mm for SD_PROB and DT_PROB)
        argument: ``-curvature %s``
desired_number_of_tracks: (an integer (int or long))
        Sets the desired number of tracks.The program will continue to
        generate tracks until this number of tracks have been selected and
        written to the output file(default is 100 for *_STREAM methods, 1000
        for *_PROB methods).
        argument: ``-number %d``
maximum_number_of_tracks: (an integer (int or long))
        Sets the maximum number of tracks to generate.The program will not
        generate more tracks than this number, even if the desired number of
        tracks hasn't yet been reached(default is 100 x number).
        argument: ``-maxnum %d``
minimum_tract_length: (a float)
        Sets the minimum length of any track in millimeters (default is 10
        mm).
        argument: ``-minlength %s``
maximum_tract_length: (a float)
        Sets the maximum length of any track in millimeters (default is 200
        mm).
        argument: ``-length %s``
cutoff_value: (a float)
        Set the FA or FOD amplitude cutoff for terminating tracks (default
        is 0.1).
        argument: ``-cutoff %s``
initial_cutoff_value: (a float)
        Sets the minimum FA or FOD amplitude for initiating tracks (default
        is twice the normal cutoff).
        argument: ``-initcutoff %s``
initial_direction: (a list of from 2 to 2 items which are an integer
          (int or long))
        Specify the initial tracking direction as a vector
        argument: ``-initdirection %s``
out_file: (a file name)
        output data file
        argument: ``%s``, position: -1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

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

SphericallyDeconvolutedStreamlineTrack

Link to code

Wraps the executable command streamtrack.

Performs streamline tracking using spherically deconvolved data

Specialized interface to StreamlineTrack. This interface is used for streamline tracking from spherically deconvolved data, and calls the MRtrix function ‘streamtrack’ with the option ‘SD_STREAM’

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> sdtrack = mrt.SphericallyDeconvolutedStreamlineTrack()
>>> sdtrack.inputs.in_file = 'data.Bfloat'
>>> sdtrack.inputs.seed_file = 'seed_mask.nii'
>>> sdtrack.run()                                          

Inputs:

[Mandatory]
in_file: (an existing file name)
        the image containing the source data.The type of data required
        depends on the type of tracking as set in the preceeding argument.
        For DT methods, the base DWI are needed. For SD methods, the SH
        harmonic coefficients of the FOD are needed.
        argument: ``%s``, position: -2

[Optional]
seed_file: (an existing file name)
        seed file
        argument: ``-seed %s``
        mutually_exclusive: seed_file, seed_spec
seed_spec: (a list of from 4 to 4 items which are a float)
        seed specification in mm and radius (x y z r)
        argument: ``-seed %s``, position: 2
        mutually_exclusive: seed_file, seed_spec
include_file: (an existing file name)
        inclusion file
        argument: ``-include %s``
        mutually_exclusive: include_file, include_spec
include_spec: (a list of from 4 to 4 items which are a float)
        inclusion specification in mm and radius (x y z r)
        argument: ``-include %s``, position: 2
        mutually_exclusive: include_file, include_spec
exclude_file: (an existing file name)
        exclusion file
        argument: ``-exclude %s``
        mutually_exclusive: exclude_file, exclude_spec
exclude_spec: (a list of from 4 to 4 items which are a float)
        exclusion specification in mm and radius (x y z r)
        argument: ``-exclude %s``, position: 2
        mutually_exclusive: exclude_file, exclude_spec
mask_file: (an existing file name)
        mask file. Only tracks within mask.
        argument: ``-mask %s``
        mutually_exclusive: mask_file, mask_spec
mask_spec: (a list of from 4 to 4 items which are a float)
        Mask specification in mm and radius (x y z r). Tracks will be
        terminated when they leave the ROI.
        argument: ``-mask %s``, position: 2
        mutually_exclusive: mask_file, mask_spec
inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
          value: DT_STREAM)
        input model type
        argument: ``%s``, position: -3
stop: (a boolean)
        stop track as soon as it enters any of the include regions.
        argument: ``-stop``
do_not_precompute: (a boolean)
        Turns off precomputation of the legendre polynomial values. Warning:
        this will slow down the algorithm by a factor of approximately 4.
        argument: ``-noprecomputed``
unidirectional: (a boolean)
        Track from the seed point in one direction only (default is to track
        in both directions).
        argument: ``-unidirectional``
no_mask_interpolation: (a boolean)
        Turns off trilinear interpolation of mask images.
        argument: ``-nomaskinterp``
step_size: (a float)
        Set the step size of the algorithm in mm (default is 0.2).
        argument: ``-step %s``
minimum_radius_of_curvature: (a float)
        Set the minimum radius of curvature (default is 2 mm for DT_STREAM,
        0 for SD_STREAM, 1 mm for SD_PROB and DT_PROB)
        argument: ``-curvature %s``
desired_number_of_tracks: (an integer (int or long))
        Sets the desired number of tracks.The program will continue to
        generate tracks until this number of tracks have been selected and
        written to the output file(default is 100 for *_STREAM methods, 1000
        for *_PROB methods).
        argument: ``-number %d``
maximum_number_of_tracks: (an integer (int or long))
        Sets the maximum number of tracks to generate.The program will not
        generate more tracks than this number, even if the desired number of
        tracks hasn't yet been reached(default is 100 x number).
        argument: ``-maxnum %d``
minimum_tract_length: (a float)
        Sets the minimum length of any track in millimeters (default is 10
        mm).
        argument: ``-minlength %s``
maximum_tract_length: (a float)
        Sets the maximum length of any track in millimeters (default is 200
        mm).
        argument: ``-length %s``
cutoff_value: (a float)
        Set the FA or FOD amplitude cutoff for terminating tracks (default
        is 0.1).
        argument: ``-cutoff %s``
initial_cutoff_value: (a float)
        Sets the minimum FA or FOD amplitude for initiating tracks (default
        is twice the normal cutoff).
        argument: ``-initcutoff %s``
initial_direction: (a list of from 2 to 2 items which are an integer
          (int or long))
        Specify the initial tracking direction as a vector
        argument: ``-initdirection %s``
out_file: (a file name)
        output data file
        argument: ``%s``, position: -1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

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

StreamlineTrack

Link to code

Wraps the executable command streamtrack.

Performs tractography using one of the following models: ‘dt_prob’, ‘dt_stream’, ‘sd_prob’, ‘sd_stream’, Where ‘dt’ stands for diffusion tensor, ‘sd’ stands for spherical deconvolution, and ‘prob’ stands for probabilistic.

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> strack = mrt.StreamlineTrack()
>>> strack.inputs.inputmodel = 'SD_PROB'
>>> strack.inputs.in_file = 'data.Bfloat'
>>> strack.inputs.seed_file = 'seed_mask.nii'
>>> strack.inputs.mask_file = 'mask.nii'
>>> strack.cmdline
'streamtrack -mask mask.nii -seed seed_mask.nii SD_PROB data.Bfloat data_tracked.tck'
>>> strack.run()                                    

Inputs:

[Mandatory]
in_file: (an existing file name)
        the image containing the source data.The type of data required
        depends on the type of tracking as set in the preceeding argument.
        For DT methods, the base DWI are needed. For SD methods, the SH
        harmonic coefficients of the FOD are needed.
        argument: ``%s``, position: -2

[Optional]
seed_file: (an existing file name)
        seed file
        argument: ``-seed %s``
        mutually_exclusive: seed_file, seed_spec
seed_spec: (a list of from 4 to 4 items which are a float)
        seed specification in mm and radius (x y z r)
        argument: ``-seed %s``, position: 2
        mutually_exclusive: seed_file, seed_spec
include_file: (an existing file name)
        inclusion file
        argument: ``-include %s``
        mutually_exclusive: include_file, include_spec
include_spec: (a list of from 4 to 4 items which are a float)
        inclusion specification in mm and radius (x y z r)
        argument: ``-include %s``, position: 2
        mutually_exclusive: include_file, include_spec
exclude_file: (an existing file name)
        exclusion file
        argument: ``-exclude %s``
        mutually_exclusive: exclude_file, exclude_spec
exclude_spec: (a list of from 4 to 4 items which are a float)
        exclusion specification in mm and radius (x y z r)
        argument: ``-exclude %s``, position: 2
        mutually_exclusive: exclude_file, exclude_spec
mask_file: (an existing file name)
        mask file. Only tracks within mask.
        argument: ``-mask %s``
        mutually_exclusive: mask_file, mask_spec
mask_spec: (a list of from 4 to 4 items which are a float)
        Mask specification in mm and radius (x y z r). Tracks will be
        terminated when they leave the ROI.
        argument: ``-mask %s``, position: 2
        mutually_exclusive: mask_file, mask_spec
inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
          value: DT_STREAM)
        input model type
        argument: ``%s``, position: -3
stop: (a boolean)
        stop track as soon as it enters any of the include regions.
        argument: ``-stop``
do_not_precompute: (a boolean)
        Turns off precomputation of the legendre polynomial values. Warning:
        this will slow down the algorithm by a factor of approximately 4.
        argument: ``-noprecomputed``
unidirectional: (a boolean)
        Track from the seed point in one direction only (default is to track
        in both directions).
        argument: ``-unidirectional``
no_mask_interpolation: (a boolean)
        Turns off trilinear interpolation of mask images.
        argument: ``-nomaskinterp``
step_size: (a float)
        Set the step size of the algorithm in mm (default is 0.2).
        argument: ``-step %s``
minimum_radius_of_curvature: (a float)
        Set the minimum radius of curvature (default is 2 mm for DT_STREAM,
        0 for SD_STREAM, 1 mm for SD_PROB and DT_PROB)
        argument: ``-curvature %s``
desired_number_of_tracks: (an integer (int or long))
        Sets the desired number of tracks.The program will continue to
        generate tracks until this number of tracks have been selected and
        written to the output file(default is 100 for *_STREAM methods, 1000
        for *_PROB methods).
        argument: ``-number %d``
maximum_number_of_tracks: (an integer (int or long))
        Sets the maximum number of tracks to generate.The program will not
        generate more tracks than this number, even if the desired number of
        tracks hasn't yet been reached(default is 100 x number).
        argument: ``-maxnum %d``
minimum_tract_length: (a float)
        Sets the minimum length of any track in millimeters (default is 10
        mm).
        argument: ``-minlength %s``
maximum_tract_length: (a float)
        Sets the maximum length of any track in millimeters (default is 200
        mm).
        argument: ``-length %s``
cutoff_value: (a float)
        Set the FA or FOD amplitude cutoff for terminating tracks (default
        is 0.1).
        argument: ``-cutoff %s``
initial_cutoff_value: (a float)
        Sets the minimum FA or FOD amplitude for initiating tracks (default
        is twice the normal cutoff).
        argument: ``-initcutoff %s``
initial_direction: (a list of from 2 to 2 items which are an integer
          (int or long))
        Specify the initial tracking direction as a vector
        argument: ``-initdirection %s``
out_file: (a file name)
        output data file
        argument: ``%s``, position: -1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

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

Tracks2Prob

Link to code

Wraps the executable command tracks2prob.

Convert a tract file into a map of the fraction of tracks to enter each voxel - also known as a tract density image (TDI) - in MRtrix’s image format (.mif). This can be viewed using MRview or converted to Nifti using MRconvert.

Example

>>> import nipype.interfaces.mrtrix as mrt
>>> tdi = mrt.Tracks2Prob()
>>> tdi.inputs.in_file = 'dwi_CSD_tracked.tck'
>>> tdi.inputs.colour = True
>>> tdi.run()                                       

Inputs:

[Mandatory]
in_file: (an existing file name)
        tract file
        argument: ``%s``, position: -2

[Optional]
template_file: (an existing file name)
        an image file to be used as a template for the output (the output
        image wil have the same transform and field of view)
        argument: ``-template %s``, position: 1
voxel_dims: (a list of from 3 to 3 items which are a float)
        Three comma-separated numbers giving the size of each voxel in mm.
        argument: ``-vox %s``, position: 2
colour: (a boolean)
        add colour to the output image according to the direction of the
        tracks.
        argument: ``-colour``, position: 3
fraction: (a boolean)
        produce an image of the fraction of fibres through each voxel (as a
        proportion of the total number in the file), rather than the count.
        argument: ``-fraction``, position: 3
output_datatype: ('Bit' or 'Int8' or 'UInt8' or 'Int16' or 'UInt16'
          or 'Int32' or 'UInt32' or 'float32' or 'float64')
        "i.e. Bfloat". Can be "char", "short", "int", "long", "float" or
        "double"
        argument: ``-datatype %s``, position: 2
resample: (a float)
        resample the tracks at regular intervals using Hermite
        interpolation. If omitted, the program will select an appropriate
        interpolation factor automatically.
        argument: ``-resample %d``, position: 3
out_filename: (a file name)
        output data file
        argument: ``%s``, position: -1
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

tract_image: (an existing file name)
        Output tract count or track density image