interfaces.mrtrix3.utils

BrainMask

Link to code

Wraps command dwi2mask

Convert a mesh surface to a partial volume estimation image

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> bmsk = mrt.BrainMask()
>>> bmsk.inputs.in_file = 'dwi.mif'
>>> bmsk.cmdline                               
'dwi2mask dwi.mif brainmask.mif'
>>> bmsk.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input diffusion weighted images
        flag: %s, position: -2
out_file: (a file name, nipype default value: brainmask.mif)
        output brain mask
        flag: %s, position: -1

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
bval_scale: (u'yes' or u'no')
        specifies whether the b - values should be scaled by the square of
        the corresponding DW gradient norm, as often required for multishell
        or DSI DW acquisition schemes. The default action can also be set in
        the MRtrix config file, under the BValueScaling entry. Valid choices
        are yes / no, true / false, 0 / 1 (default: true).
        flag: -bvalue_scaling %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
grad_file: (an existing file name)
        dw gradient scheme (MRTrix format
        flag: -grad %s
grad_fsl: (a tuple of the form: (an existing file name, an existing
         file name))
        (bvecs, bvals) dw gradient scheme (FSL format
        flag: -fslgrad %s %s
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_bval: (an existing file name)
        bvals file in FSL format
in_bvec: (an existing file name)
        bvecs file in FSL format
        flag: -fslgrad %s %s
nthreads: (an integer (int or long))
        number of threads. if zero, the number of available cpus will be
        used
        flag: -nthreads %d
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        the output response file

ComputeTDI

Link to code

Wraps command tckmap

Use track data as a form of contrast for producing a high-resolution image.

References

  • For TDI or DEC TDI: Calamante, F.; Tournier, J.-D.; Jackson, G. D. & Connelly, A. Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage, 2010, 53, 1233-1243
  • If using -contrast length and -stat_vox mean: Pannek, K.; Mathias, J. L.; Bigler, E. D.; Brown, G.; Taylor, J. D. & Rose, S. E. The average pathlength map: A diffusion MRI tractography-derived index for studying brain pathology. NeuroImage, 2011, 55, 133-141
  • If using -dixel option with TDI contrast only: Smith, R.E., Tournier, J-D., Calamante, F., Connelly, A. A novel paradigm for automated segmentation of very large whole-brain probabilistic tractography data sets. In proc. ISMRM, 2011, 19, 673
  • If using -dixel option with any other contrast: Pannek, K., Raffelt, D., Salvado, O., Rose, S. Incorporating directional information in diffusion tractography derived maps: angular track imaging (ATI). In Proc. ISMRM, 2012, 20, 1912
  • If using -tod option: Dhollander, T., Emsell, L., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P. Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography. NeuroImage, 2014, 94, 312-336
  • If using other contrasts / statistics: Calamante, F.; Tournier, J.-D.; Smith, R. E. & Connelly, A. A generalised framework for super-resolution track-weighted imaging. NeuroImage, 2012, 59, 2494-2503
  • If using -precise mapping option: Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. SIFT: Spherical-deconvolution informed filtering of tractograms. NeuroImage, 2013, 67, 298-312 (Appendix 3)

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> tdi = mrt.ComputeTDI()
>>> tdi.inputs.in_file = 'dti.mif'
>>> tdi.cmdline                               
'tckmap dti.mif tdi.mif'
>>> tdi.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input tractography
        flag: %s, position: -2

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
contrast: (u'tdi' or u'length' or u'invlength' or u'scalar_map' or
         u'scalar_map_conut' or u'fod_amp' or u'curvature')
        define the desired form of contrast for the output image
        flag: -constrast %s
data_type: (u'float' or u'unsigned int')
        specify output image data type
        flag: -datatype %s
dixel: (a file name)
        map streamlines todixels within each voxel. Directions are stored
        asazimuth elevation pairs.
        flag: -dixel %s
ends_only: (a boolean)
        only map the streamline endpoints to the image
        flag: -ends_only
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
fwhm_tck: (a float)
        define the statistic for choosing the contribution to be made by
        each streamline as a function of the samples taken along their
        lengths
        flag: -fwhm_tck %f
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_map: (an existing file name)
        provide thescalar image map for generating images with 'scalar_map'
        contrasts, or the SHs image for fod_amp
        flag: -image %s
map_zero: (a boolean)
        if a streamline has zero contribution based on the contrast &
        statistic, typically it is not mapped; use this option to still
        contribute to the map even if this is the case (these non-
        contributing voxels can then influence the mean value in each voxel
        of the map)
        flag: -map_zero
max_tod: (an integer (int or long))
        generate a Track Orientation Distribution (TOD) in each voxel.
        flag: -tod %d
nthreads: (an integer (int or long))
        number of threads. if zero, the number of available cpus will be
        used
        flag: -nthreads %d
out_file: (a file name, nipype default value: tdi.mif)
        output TDI file
        flag: %s, position: -1
precise: (a boolean)
        use a more precise streamline mapping strategy, that accurately
        quantifies the length through each voxel (these lengths are then
        taken into account during TWI calculation)
        flag: -precise
reference: (an existing file name)
        a referenceimage to be used as template
        flag: -template %s
stat_tck: (u'mean' or u'sum' or u'min' or u'max' or u'median' or
         u'mean_nonzero' or u'gaussian' or u'ends_min' or u'ends_mean' or
         u'ends_max' or u'ends_prod')
        define the statistic for choosing the contribution to be made by
        each streamline as a function of the samples taken along their
        lengths.
        flag: -stat_tck %s
stat_vox: (u'sum' or u'min' or u'mean' or u'max')
        define the statistic for choosing the finalvoxel intesities for a
        given contrast
        flag: -stat_vox %s
tck_weights: (an existing file name)
        specify a text scalar file containing the streamline weights
        flag: -tck_weights_in %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
upsample: (an integer (int or long))
        upsample the tracks by some ratio using Hermite interpolation before
        mappping
        flag: -upsample %d
use_dec: (a boolean)
        perform mapping in DEC space
        flag: -dec
vox_size: (a list of items which are an integer (int or long))
        voxel dimensions
        flag: -vox %s

Outputs:

out_file: (a file name)
        output TDI file

Generate5tt

Link to code

Wraps command 5ttgen

Concatenate segmentation results from FSL FAST and FIRST into the 5TT format required for ACT

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> seg = mrt.Generate5tt()
>>> seg.inputs.in_fast = ['tpm_00.nii.gz',
...                       'tpm_01.nii.gz', 'tpm_02.nii.gz']
>>> seg.inputs.in_first = 'first_merged.nii.gz'
>>> seg.cmdline                               
'5ttgen tpm_00.nii.gz tpm_01.nii.gz tpm_02.nii.gz first_merged.nii.gz act-5tt.mif'
>>> seg.run()                                 

Inputs:

[Mandatory]
in_fast: (a list of items which are an existing file name)
        list of PVE images from FAST
        flag: %s, position: -3
out_file: (a file name, nipype default value: act-5tt.mif)
        name of output file
        flag: %s, position: -1

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
in_first: (an existing file name)
        combined segmentation file from FIRST
        flag: %s, position: -2
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        segmentation for ACT in 5tt format

Mesh2PVE

Link to code

Wraps command mesh2pve

Convert a mesh surface to a partial volume estimation image

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> m2p = mrt.Mesh2PVE()
>>> m2p.inputs.in_file = 'surf1.vtk'
>>> m2p.inputs.reference = 'dwi.mif'
>>> m2p.inputs.in_first = 'T1.nii.gz'
>>> m2p.cmdline                               
'mesh2pve -first T1.nii.gz surf1.vtk dwi.mif mesh2volume.nii.gz'
>>> m2p.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input mesh
        flag: %s, position: -3
out_file: (a file name, nipype default value: mesh2volume.nii.gz)
        output file containing SH coefficients
        flag: %s, position: -1
reference: (an existing file name)
        input reference image
        flag: %s, position: -2

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
in_first: (an existing file name)
        indicates that the mesh file is provided by FSL FIRST
        flag: -first %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        the output response file

TCK2VTK

Link to code

Wraps command tck2vtk

Convert a track file to a vtk format, cave: coordinates are in XYZ coordinates not reference

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> vtk = mrt.TCK2VTK()
>>> vtk.inputs.in_file = 'tracks.tck'
>>> vtk.inputs.reference = 'b0.nii'
>>> vtk.cmdline                               
'tck2vtk -image b0.nii tracks.tck tracks.vtk'
>>> vtk.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input tractography
        flag: %s, position: -2

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
nthreads: (an integer (int or long))
        number of threads. if zero, the number of available cpus will be
        used
        flag: -nthreads %d
out_file: (a file name, nipype default value: tracks.vtk)
        output VTK file
        flag: %s, position: -1
reference: (an existing file name)
        if specified, the properties of this image will be used to convert
        track point positions from real (scanner) coordinates into image
        coordinates (in mm).
        flag: -image %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
voxel: (an existing file name)
        if specified, the properties of this image will be used to convert
        track point positions from real (scanner) coordinates into image
        coordinates.
        flag: -image %s

Outputs:

out_file: (a file name)
        output VTK file

TensorMetrics

Link to code

Wraps command tensor2metric

Compute metrics from tensors

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> comp = mrt.TensorMetrics()
>>> comp.inputs.in_file = 'dti.mif'
>>> comp.inputs.out_fa = 'fa.mif'
>>> comp.cmdline                               
'tensor2metric -fa fa.mif dti.mif'
>>> comp.run()                                 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input DTI image
        flag: %s, position: -1

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
component: (a list of items which are any value)
        specify the desired eigenvalue/eigenvector(s). Note that several
        eigenvalues can be specified as a number sequence
        flag: -num %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
in_mask: (an existing file name)
        only perform computation within the specified binary brain mask
        image
        flag: -mask %s
modulate: (u'FA' or u'none' or u'eval')
        how to modulate the magnitude of the eigenvectors
        flag: -modulate %s
out_adc: (a file name)
        output ADC file
        flag: -adc %s
out_eval: (a file name)
        output selected eigenvalue(s) file
        flag: -value %s
out_evec: (a file name)
        output selected eigenvector(s) file
        flag: -vector %s
out_fa: (a file name)
        output FA file
        flag: -fa %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_adc: (a file name)
        output ADC file
out_eval: (a file name)
        output selected eigenvalue(s) file
out_evec: (a file name)
        output selected eigenvector(s) file
out_fa: (a file name)
        output FA file