nipype.interfaces.mrtrix3.utils module

BrainMask

Link to code

Bases: CommandLine

Wrapped executable: 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                               # doctest: +ELLIPSIS
'dwi2mask dwi.mif brainmask.mif'
>>> bmsk.run()                                 # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input diffusion weighted images. Maps to a command-line argument: %s (position: -2).
out_file : a pathlike object or string representing a file
Output brain mask. Maps to a command-line argument: %s (position: -1). (Nipype default value: brainmask.mif)
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing an existing file
The output response file.

ComputeTDI

Link to code

Bases: MRTrix3Base

Wrapped executable: 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                               # doctest: +ELLIPSIS
'tckmap dti.mif tdi.mif'
>>> tdi.run()                                 # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input tractography. Maps to a command-line argument: %s (position: -2).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
contrast : ‘tdi’ or ‘length’ or ‘invlength’ or ‘scalar_map’ or ‘scalar_map_conut’ or ‘fod_amp’ or ‘curvature’
Define the desired form of contrast for the output image. Maps to a command-line argument: -constrast %s.
data_type : ‘float’ or ‘unsigned int’
Specify output image data type. Maps to a command-line argument: -datatype %s.
dixel : a pathlike object or string representing a file
Map streamlines todixels within each voxel. Directions are stored asazimuth elevation pairs. Maps to a command-line argument: -dixel %s.
ends_only : a boolean
Only map the streamline endpoints to the image. Maps to a command-line argument: -ends_only.
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’
Environment variables. (Nipype default value: {})
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. Maps to a command-line argument: -fwhm_tck %f.
in_map : a pathlike object or string representing an existing file
Provide thescalar image map for generating images with ‘scalar_map’ contrasts, or the SHs image for fod_amp. Maps to a command-line argument: -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). Maps to a command-line argument: -map_zero.
max_tod : an integer (int or long)
Generate a Track Orientation Distribution (TOD) in each voxel. Maps to a command-line argument: -tod %d.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing a file
Output TDI file. Maps to a command-line argument: %s (position: -1). (Nipype default value: tdi.mif)
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). Maps to a command-line argument: -precise.
reference : a pathlike object or string representing an existing file
A referenceimage to be used as template. Maps to a command-line argument: -template %s.
stat_tck : ‘mean’ or ‘sum’ or ‘min’ or ‘max’ or ‘median’ or ‘mean_nonzero’ or ‘gaussian’ or ‘ends_min’ or ‘ends_mean’ or ‘ends_max’ or ‘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. Maps to a command-line argument: -stat_tck %s.
stat_vox : ‘sum’ or ‘min’ or ‘mean’ or ‘max’
Define the statistic for choosing the finalvoxel intesities for a given contrast. Maps to a command-line argument: -stat_vox %s.
tck_weights : a pathlike object or string representing an existing file
Specify a text scalar file containing the streamline weights. Maps to a command-line argument: -tck_weights_in %s.
upsample : an integer (int or long)
Upsample the tracks by some ratio using Hermite interpolation before mappping. Maps to a command-line argument: -upsample %d.
use_dec : a boolean
Perform mapping in DEC space. Maps to a command-line argument: -dec.
vox_size : a list of items which are an integer (int or long)
Voxel dimensions. Maps to a command-line argument: -vox %s.
out_file : a pathlike object or string representing a file
Output TDI file.

DWIExtract

Link to code

Bases: MRTrix3Base

Wrapped executable: dwiextract.

Extract diffusion-weighted volumes, b=0 volumes, or certain shells from a DWI dataset

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> dwiextract = mrt.DWIExtract()
>>> dwiextract.inputs.in_file = 'dwi.mif'
>>> dwiextract.inputs.bzero = True
>>> dwiextract.inputs.out_file = 'b0vols.mif'
>>> dwiextract.inputs.grad_fsl = ('bvecs', 'bvals')
>>> dwiextract.cmdline                             # doctest: +ELLIPSIS
'dwiextract -bzero -fslgrad bvecs bvals dwi.mif b0vols.mif'
>>> dwiextract.run()                               # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input image. Maps to a command-line argument: %s (position: -2).
out_file : a pathlike object or string representing a file
Output image. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %s.
bzero : a boolean
Extract b=0 volumes. Maps to a command-line argument: -bzero.
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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
nobzero : a boolean
Extract non b=0 volumes. Maps to a command-line argument: -no_bzero.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
shell : a list of items which are a float
Specify one or more gradient shells. Maps to a command-line argument: -shell %s.
singleshell : a boolean
Extract volumes with a specific shell. Maps to a command-line argument: -singleshell.
out_file : a pathlike object or string representing an existing file
Output image.

Generate5tt

Link to code

Bases: MRTrix3Base

Wrapped executable: 5ttgen.

Generate a 5TT image suitable for ACT using the selected algorithm

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> gen5tt = mrt.Generate5tt()
>>> gen5tt.inputs.in_file = 'T1.nii.gz'
>>> gen5tt.inputs.algorithm = 'fsl'
>>> gen5tt.inputs.out_file = '5tt.mif'
>>> gen5tt.cmdline                             # doctest: +ELLIPSIS
'5ttgen fsl T1.nii.gz 5tt.mif'
>>> gen5tt.run()                               # doctest: +SKIP
algorithm : ‘fsl’ or ‘gif’ or ‘freesurfer’
Tissue segmentation algorithm. Maps to a command-line argument: %s (position: -3).
in_file : a pathlike object or string representing an existing file
Input image. Maps to a command-line argument: %s (position: -2).
out_file : a pathlike object or string representing a file
Output image. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing an existing file
Output image.

MRConvert

Link to code

Bases: MRTrix3Base

Wrapped executable: mrconvert.

Perform conversion between different file types and optionally extract a subset of the input image

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> mrconvert = mrt.MRConvert()
>>> mrconvert.inputs.in_file = 'dwi.nii.gz'
>>> mrconvert.inputs.grad_fsl = ('bvecs', 'bvals')
>>> mrconvert.cmdline                             # doctest: +ELLIPSIS
'mrconvert -fslgrad bvecs bvals dwi.nii.gz dwi.mif'
>>> mrconvert.run()                               # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input image. Maps to a command-line argument: %s (position: -2).
out_file : a pathlike object or string representing a file
Output image. Maps to a command-line argument: %s (position: -1). (Nipype default value: dwi.mif)
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
axes : a list of items which are an integer (int or long)
Specify the axes that will be used. Maps to a command-line argument: -axes %s.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %s.
coord : a list of items which are a float
Extract data at the specified coordinates. Maps to a command-line argument: -coord %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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
scaling : a list of items which are a float
Specify the data scaling parameter. Maps to a command-line argument: -scaling %s.
vox : a list of items which are a float
Change the voxel dimensions. Maps to a command-line argument: -vox %s.
out_file : a pathlike object or string representing an existing file
Output image.

MRMath

Link to code

Bases: MRTrix3Base

Wrapped executable: mrmath.

Compute summary statistic on image intensities along a specified axis of a single image

Example

>>> import nipype.interfaces.mrtrix3 as mrt
>>> mrmath = mrt.MRMath()
>>> mrmath.inputs.in_file = 'dwi.mif'
>>> mrmath.inputs.operation = 'mean'
>>> mrmath.inputs.axis = 3
>>> mrmath.inputs.out_file = 'dwi_mean.mif'
>>> mrmath.inputs.grad_fsl = ('bvecs', 'bvals')
>>> mrmath.cmdline                             # doctest: +ELLIPSIS
'mrmath -axis 3 -fslgrad bvecs bvals dwi.mif mean dwi_mean.mif'
>>> mrmath.run()                               # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input image. Maps to a command-line argument: %s (position: -3).
operation : ‘mean’ or ‘median’ or ‘sum’ or ‘product’ or ‘rms’ or ‘norm’ or ‘var’ or ‘std’ or ‘min’ or ‘max’ or ‘absmax’ or ‘magmax’
Operation to computer along a specified axis. Maps to a command-line argument: %s (position: -2).
out_file : a pathlike object or string representing a file
Output image. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
axis : an integer (int or long)
Specfied axis to perform the operation along. Maps to a command-line argument: -axis %d.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing an existing file
Output image.

MRResize

Link to code

Bases: MRTrix3Base

Wrapped executable: mrresize.

Resize an image by defining the new image resolution, voxel size or a scale factor. If the image is 4D, then only the first 3 dimensions can be resized. Also, if the image is down-sampled, the appropriate smoothing is automatically applied using Gaussian smoothing. For more information, see <https://mrtrix.readthedocs.io/en/latest/reference/commands/mrresize.html>

Example

>>> import nipype.interfaces.mrtrix3 as mrt

Defining the new image resolution: >>> image_resize = mrt.MRResize() >>> image_resize.inputs.in_file = ‘dwi.mif’ >>> image_resize.inputs.image_size = (256, 256, 144) >>> image_resize.cmdline # doctest: +ELLIPSIS ‘mrresize -size 256,256,144 -interp cubic dwi.mif dwi_resized.mif’ >>> image_resize.run() # doctest: +SKIP

Defining the new image’s voxel size: >>> voxel_resize = mrt.MRResize() >>> voxel_resize.inputs.in_file = ‘dwi.mif’ >>> voxel_resize.inputs.voxel_size = (1, 1, 1) >>> voxel_resize.cmdline # doctest: +ELLIPSIS ‘mrresize -interp cubic -voxel 1,1,1 dwi.mif dwi_resized.mif’ >>> voxel_resize.run() # doctest: +SKIP

Defining the scale factor of each image dimension: >>> scale_resize = mrt.MRResize() >>> scale_resize.inputs.in_file = ‘dwi.mif’ >>> scale_resize.inputs.scale_factor = (0.5,0.5,0.5) >>> scale_resize.cmdline # doctest: +ELLIPSIS ‘mrresize -interp cubic -scale 0.5,0.5,0.5 dwi.mif dwi_resized.mif’ >>> scale_resize.run() # doctest: +SKIP

image_size : a tuple of the form: (an integer (int or long), an integer (int or long), an integer (int or long))
Number of voxels in each dimension of output image. Maps to a command-line argument: -size %d,%d,%d. Mutually exclusive with inputs: voxel_size, scale_factor.
in_file : a pathlike object or string representing an existing file
Input DWI image. Maps to a command-line argument: %s (position: -2).
scale_factor : a tuple of the form: (a float, a float, a float)
Scale factors to rescale the image by in each dimension. Maps to a command-line argument: -scale %g,%g,%g. Mutually exclusive with inputs: image_size, voxel_size.
voxel_size : a tuple of the form: (a float, a float, a float)
Desired voxel size in mm for the output image. Maps to a command-line argument: -voxel %g,%g,%g. Mutually exclusive with inputs: image_size, scale_factor.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bval_scale : ‘yes’ or ‘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). Maps to a command-line argument: -bvalue_scaling %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’
Environment variables. (Nipype default value: {})
grad_file : a pathlike object or string representing an existing file
Dw gradient scheme (MRTrix format). Maps to a command-line argument: -grad %s. Mutually exclusive with inputs: grad_fsl.
grad_fsl : a tuple of the form: (a pathlike object or string representing an existing file, a pathlike object or string representing an existing file)
(bvecs, bvals) dw gradient scheme (FSL format). Maps to a command-line argument: -fslgrad %s %s. Mutually exclusive with inputs: grad_file.
in_bval : a pathlike object or string representing an existing file
Bvals file in FSL format.
in_bvec : a pathlike object or string representing an existing file
Bvecs file in FSL format. Maps to a command-line argument: -fslgrad %s %s.
interpolation : ‘cubic’ or ‘nearest’ or ‘linear’ or ‘sinc’
Set the interpolation method to use when resizing (choices: nearest, linear, cubic, sinc. Default: cubic). Maps to a command-line argument: -interp %s. (Nipype default value: cubic)
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing a file
The output resized DWI image. Maps to a command-line argument: %s (position: -1).
out_file : a pathlike object or string representing an existing file
The output resized DWI image.

Mesh2PVE

Link to code

Bases: CommandLine

Wrapped executable: 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                               # doctest: +ELLIPSIS
'mesh2pve -first T1.nii.gz surf1.vtk dwi.mif mesh2volume.nii.gz'
>>> m2p.run()                                 # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input mesh. Maps to a command-line argument: %s (position: -3).
out_file : a pathlike object or string representing a file
Output file containing SH coefficients. Maps to a command-line argument: %s (position: -1). (Nipype default value: mesh2volume.nii.gz)
reference : a pathlike object or string representing an existing file
Input reference image. Maps to a command-line argument: %s (position: -2).
args : a unicode string
Additional parameters to the command. Maps to a command-line 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’
Environment variables. (Nipype default value: {})
in_first : a pathlike object or string representing an existing file
Indicates that the mesh file is provided by FSL FIRST. Maps to a command-line argument: -first %s.
out_file : a pathlike object or string representing an existing file
The output response file.

TCK2VTK

Link to code

Bases: MRTrix3Base

Wrapped executable: 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                               # doctest: +ELLIPSIS
'tck2vtk -image b0.nii tracks.tck tracks.vtk'
>>> vtk.run()                                 # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input tractography. Maps to a command-line argument: %s (position: -2).
args : a unicode string
Additional parameters to the command. Maps to a command-line 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’
Environment variables. (Nipype default value: {})
nthreads : an integer (int or long)
Number of threads. if zero, the number of available cpus will be used. Maps to a command-line argument: -nthreads %d.
out_file : a pathlike object or string representing a file
Output VTK file. Maps to a command-line argument: %s (position: -1). (Nipype default value: tracks.vtk)
reference : a pathlike object or string representing an existing file
If specified, the properties of this image will be used to convert track point positions from real (scanner) coordinates into image coordinates (in mm). Maps to a command-line argument: -image %s.
voxel : a pathlike object or string representing an existing file
If specified, the properties of this image will be used to convert track point positions from real (scanner) coordinates into image coordinates. Maps to a command-line argument: -image %s.
out_file : a pathlike object or string representing a file
Output VTK file.

TensorMetrics

Link to code

Bases: CommandLine

Wrapped executable: 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                               # doctest: +ELLIPSIS
'tensor2metric -num 1 -fa fa.mif dti.mif'
>>> comp.run()                                 # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input DTI image. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %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. Maps to a command-line argument: -num %s. (Nipype default value: [1])
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’
Environment variables. (Nipype default value: {})
in_mask : a pathlike object or string representing an existing file
Only perform computation within the specified binary brain mask image. Maps to a command-line argument: -mask %s.
modulate : ‘FA’ or ‘none’ or ‘eval’
How to modulate the magnitude of the eigenvectors. Maps to a command-line argument: -modulate %s.
out_adc : a pathlike object or string representing a file
Output ADC file. Maps to a command-line argument: -adc %s.
out_eval : a pathlike object or string representing a file
Output selected eigenvalue(s) file. Maps to a command-line argument: -value %s.
out_evec : a pathlike object or string representing a file
Output selected eigenvector(s) file. Maps to a command-line argument: -vector %s.
out_fa : a pathlike object or string representing a file
Output FA file. Maps to a command-line argument: -fa %s.
out_adc : a pathlike object or string representing a file
Output ADC file.
out_eval : a pathlike object or string representing a file
Output selected eigenvalue(s) file.
out_evec : a pathlike object or string representing a file
Output selected eigenvector(s) file.
out_fa : a pathlike object or string representing a file
Output FA file.