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# interfaces.mrtrix.tracking

## DiffusionTensorStreamlineTrack

Link to code

Wraps 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
    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.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
    cutoff_value: (a float)
            Set the FA or FOD amplitude cutoff for terminating tracks (default
            is 0.1).
    desired_number_of_tracks: (an integer)
            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).
    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.
    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
    exclude_file: (an existing file name)
            exclusion file
    exclude_spec: (a list of from 4 to 4 items which are a float)
            exclusion specification in mm and radius (x y z r)
    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
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    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.
    include_file: (an existing file name)
            inclusion file
    include_spec: (a list of from 4 to 4 items which are a float)
            inclusion specification in mm and radius (x y z r)
    initial_cutoff_value: (a float)
            Sets the minimum FA or FOD amplitude for initiating tracks (default
            is twice the normal cutoff).
    initial_direction: (a list of from 2 to 2 items which are an integer)
            Specify the initial tracking direction as a vector
    inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
             value: DT_STREAM)
            input model type
    mask_file: (an existing file name)
            mask file. Only tracks within mask.
    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.
    maximum_number_of_tracks: (an integer)
            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).
    maximum_tract_length: (a float)
            Sets the maximum length of any track in millimeters (default is 200
            mm).
    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)
    minimum_tract_length: (a float)
            Sets the minimum length of any track in millimeters (default is 10
            mm).
    no_mask_interpolation: (a boolean)
            Turns off trilinear interpolation of mask images.
    out_file: (a file name)
            output data file
    seed_file: (an existing file name)
            seed file
    seed_spec: (a list of from 4 to 4 items which are a float)
            seed specification in mm and radius (x y z r)
    step_size: (a float)
            Set the step size of the algorithm in mm (default is 0.2).
    stop: (a boolean)
            stop track as soon as it enters any of the include regions.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    unidirectional: (a boolean)
            Track from the seed point in one direction only (default is to track
            in both directions).

Outputs:

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

## ProbabilisticSphericallyDeconvolutedStreamlineTrack

Link to code

Wraps 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.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
    cutoff_value: (a float)
            Set the FA or FOD amplitude cutoff for terminating tracks (default
            is 0.1).
    desired_number_of_tracks: (an integer)
            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).
    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.
    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
    exclude_file: (an existing file name)
            exclusion file
    exclude_spec: (a list of from 4 to 4 items which are a float)
            exclusion specification in mm and radius (x y z r)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    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.
    include_file: (an existing file name)
            inclusion file
    include_spec: (a list of from 4 to 4 items which are a float)
            inclusion specification in mm and radius (x y z r)
    initial_cutoff_value: (a float)
            Sets the minimum FA or FOD amplitude for initiating tracks (default
            is twice the normal cutoff).
    initial_direction: (a list of from 2 to 2 items which are an integer)
            Specify the initial tracking direction as a vector
    inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
             value: DT_STREAM)
            input model type
    mask_file: (an existing file name)
            mask file. Only tracks within mask.
    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.
    maximum_number_of_tracks: (an integer)
            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).
    maximum_number_of_trials: (an integer)
            Set the maximum number of sampling trials at each point (only used
            for probabilistic tracking).
    maximum_tract_length: (a float)
            Sets the maximum length of any track in millimeters (default is 200
            mm).
    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)
    minimum_tract_length: (a float)
            Sets the minimum length of any track in millimeters (default is 10
            mm).
    no_mask_interpolation: (a boolean)
            Turns off trilinear interpolation of mask images.
    out_file: (a file name)
            output data file
    seed_file: (an existing file name)
            seed file
    seed_spec: (a list of from 4 to 4 items which are a float)
            seed specification in mm and radius (x y z r)
    step_size: (a float)
            Set the step size of the algorithm in mm (default is 0.2).
    stop: (a boolean)
            stop track as soon as it enters any of the include regions.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    unidirectional: (a boolean)
            Track from the seed point in one direction only (default is to track
            in both directions).

Outputs:

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

## SphericallyDeconvolutedStreamlineTrack

Link to code

Wraps 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.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
    cutoff_value: (a float)
            Set the FA or FOD amplitude cutoff for terminating tracks (default
            is 0.1).
    desired_number_of_tracks: (an integer)
            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).
    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.
    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
    exclude_file: (an existing file name)
            exclusion file
    exclude_spec: (a list of from 4 to 4 items which are a float)
            exclusion specification in mm and radius (x y z r)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    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.
    include_file: (an existing file name)
            inclusion file
    include_spec: (a list of from 4 to 4 items which are a float)
            inclusion specification in mm and radius (x y z r)
    initial_cutoff_value: (a float)
            Sets the minimum FA or FOD amplitude for initiating tracks (default
            is twice the normal cutoff).
    initial_direction: (a list of from 2 to 2 items which are an integer)
            Specify the initial tracking direction as a vector
    inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
             value: DT_STREAM)
            input model type
    mask_file: (an existing file name)
            mask file. Only tracks within mask.
    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.
    maximum_number_of_tracks: (an integer)
            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).
    maximum_tract_length: (a float)
            Sets the maximum length of any track in millimeters (default is 200
            mm).
    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)
    minimum_tract_length: (a float)
            Sets the minimum length of any track in millimeters (default is 10
            mm).
    no_mask_interpolation: (a boolean)
            Turns off trilinear interpolation of mask images.
    out_file: (a file name)
            output data file
    seed_file: (an existing file name)
            seed file
    seed_spec: (a list of from 4 to 4 items which are a float)
            seed specification in mm and radius (x y z r)
    step_size: (a float)
            Set the step size of the algorithm in mm (default is 0.2).
    stop: (a boolean)
            stop track as soon as it enters any of the include regions.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    unidirectional: (a boolean)
            Track from the seed point in one direction only (default is to track
            in both directions).

Outputs:

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

## StreamlineTrack

Link to code

Wraps 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.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.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
    cutoff_value: (a float)
            Set the FA or FOD amplitude cutoff for terminating tracks (default
            is 0.1).
    desired_number_of_tracks: (an integer)
            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).
    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.
    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
    exclude_file: (an existing file name)
            exclusion file
    exclude_spec: (a list of from 4 to 4 items which are a float)
            exclusion specification in mm and radius (x y z r)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    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.
    include_file: (an existing file name)
            inclusion file
    include_spec: (a list of from 4 to 4 items which are a float)
            inclusion specification in mm and radius (x y z r)
    initial_cutoff_value: (a float)
            Sets the minimum FA or FOD amplitude for initiating tracks (default
            is twice the normal cutoff).
    initial_direction: (a list of from 2 to 2 items which are an integer)
            Specify the initial tracking direction as a vector
    inputmodel: ('DT_STREAM' or 'SD_PROB' or 'SD_STREAM', nipype default
             value: DT_STREAM)
            input model type
    mask_file: (an existing file name)
            mask file. Only tracks within mask.
    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.
    maximum_number_of_tracks: (an integer)
            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).
    maximum_tract_length: (a float)
            Sets the maximum length of any track in millimeters (default is 200
            mm).
    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)
    minimum_tract_length: (a float)
            Sets the minimum length of any track in millimeters (default is 10
            mm).
    no_mask_interpolation: (a boolean)
            Turns off trilinear interpolation of mask images.
    out_file: (a file name)
            output data file
    seed_file: (an existing file name)
            seed file
    seed_spec: (a list of from 4 to 4 items which are a float)
            seed specification in mm and radius (x y z r)
    step_size: (a float)
            Set the step size of the algorithm in mm (default is 0.2).
    stop: (a boolean)
            stop track as soon as it enters any of the include regions.
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    unidirectional: (a boolean)
            Track from the seed point in one direction only (default is to track
            in both directions).

Outputs:

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

## Tracks2Prob

Link to code

Wraps 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
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
    colour: (a boolean)
            add colour to the output image according to the direction of the
            tracks.
    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
    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.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_file: (an existing file name)
            tract file
    out_filename: (a file name)
            output data file
    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"
    resample: (a float)
            resample the tracks at regular intervals using Hermite
            interpolation. If omitted, the program will select an appropriate
            interpolation factor automatically.
    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)
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    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.

Outputs:

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