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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