interfaces.mrtrix3.utils¶
BrainMask¶
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 # doctest: +ELLIPSIS
'dwi2mask dwi.mif brainmask.mif'
>>> bmsk.run() # doctest: +SKIP
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: ('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).
flag: -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', 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
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
Outputs:
out_file: (an existing file name)
the output response file
ComputeTDI¶
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 # doctest: +ELLIPSIS
'tckmap dti.mif tdi.mif'
>>> tdi.run() # doctest: +SKIP
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: ('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
flag: -constrast %s
data_type: ('float' or '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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
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
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: ('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.
flag: -stat_tck %s
stat_vox: ('sum' or 'min' or 'mean' or '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
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
DWIExtract¶
Wraps command 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
Inputs:
[Mandatory]
in_file: (an existing file name)
input image
flag: %s, position: -2
out_file: (a file name)
output image
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %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).
flag: -bvalue_scaling %s
bzero: (a boolean)
extract b=0 volumes
flag: -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', 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
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
nobzero: (a boolean)
extract non b=0 volumes
flag: -no_bzero
nthreads: (an integer (int or long))
number of threads. if zero, the number of available cpus will be
used
flag: -nthreads %d
shell: (a list of items which are a float)
specify one or more gradient shells
flag: -shell %s
singleshell: (a boolean)
extract volumes with a specific shell
flag: -singleshell
Outputs:
out_file: (an existing file name)
output image
Generate5tt¶
Wraps command 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
Inputs:
[Mandatory]
algorithm: ('fsl' or 'gif' or 'freesurfer')
tissue segmentation algorithm
flag: %s, position: -3
in_file: (an existing file name)
input image
flag: %s, position: -2
out_file: (a file name)
output image
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %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).
flag: -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', 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
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
Outputs:
out_file: (an existing file name)
output image
MRConvert¶
Wraps command 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
Inputs:
[Mandatory]
in_file: (an existing file name)
input image
flag: %s, position: -2
out_file: (a file name, nipype default value: dwi.mif)
output image
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
axes: (a list of items which are an integer (int or long))
specify the axes that will be used
flag: -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).
flag: -bvalue_scaling %s
coord: (a list of items which are a float)
extract data at the specified coordinates
flag: -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', 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
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
scaling: (a list of items which are a float)
specify the data scaling parameter
flag: -scaling %s
vox: (a list of items which are a float)
change the voxel dimensions
flag: -vox %s
Outputs:
out_file: (an existing file name)
output image
MRMath¶
Wraps command 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
Inputs:
[Mandatory]
in_file: (an existing file name)
input image
flag: %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
flag: %s, position: -2
out_file: (a file name)
output image
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
axis: (an integer (int or long))
specfied axis to perform the operation along
flag: -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).
flag: -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', 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
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
Outputs:
out_file: (an existing file name)
output image
Mesh2PVE¶
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 # doctest: +ELLIPSIS
'mesh2pve -first T1.nii.gz surf1.vtk dwi.mif mesh2volume.nii.gz'
>>> m2p.run() # doctest: +SKIP
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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
in_first: (an existing file name)
indicates that the mesh file is provided by FSL FIRST
flag: -first %s
Outputs:
out_file: (an existing file name)
the output response file
TCK2VTK¶
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 # doctest: +ELLIPSIS
'tck2vtk -image b0.nii tracks.tck tracks.vtk'
>>> vtk.run() # doctest: +SKIP
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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
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
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¶
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 # doctest: +ELLIPSIS
'tensor2metric -num 1 -fa fa.mif dti.mif'
>>> comp.run() # doctest: +SKIP
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, nipype default
value: [1])
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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
in_mask: (an existing file name)
only perform computation within the specified binary brain mask
image
flag: -mask %s
modulate: ('FA' or 'none' or '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
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