interfaces.mrtrix.preprocess¶
DWI2Tensor¶
Wraps the executable command dwi2tensor
.
Converts diffusion-weighted images to tensor images.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> dwi2tensor = mrt.DWI2Tensor()
>>> dwi2tensor.inputs.in_file = 'dwi.mif'
>>> dwi2tensor.inputs.encoding_file = 'encoding.txt'
>>> dwi2tensor.cmdline
'dwi2tensor -grad encoding.txt dwi.mif dwi_tensor.mif'
>>> dwi2tensor.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a list of items which are a pathlike object or string
representing an existing file)
Diffusion-weighted images
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output tensor filename
argument: ``%s``, position: -1
encoding_file: (a pathlike object or string representing a file)
Encoding file 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()
argument: ``-grad %s``, position: 2
ignore_slice_by_volume: (a list of from 2 to 2 items which are an
integer (int or long))
Requires two values (i.e. [34 1] for [Slice Volume] Ignores the
image slices specified when computing the tensor. Slice here means
the z coordinate of the slice to be ignored.
argument: ``-ignoreslices %s``, position: 2
ignore_volumes: (a list of at least 1 items which are an integer (int
or long))
Requires two values (i.e. [2 5 6] for [Volumes] Ignores the image
volumes specified when computing the tensor.
argument: ``-ignorevolumes %s``, position: 2
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
tensor: (a pathlike object or string representing an existing file)
path/name of output diffusion tensor image
Erode¶
Wraps the executable command erode
.
Erode (or dilates) a mask (i.e. binary) image
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> erode = mrt.Erode()
>>> erode.inputs.in_file = 'mask.mif'
>>> erode.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Input mask image to be eroded
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output image filename
argument: ``%s``, position: -1
number_of_passes: (an integer (int or long))
the number of passes (default: 1)
argument: ``-npass %s``
dilate: (a boolean)
Perform dilation rather than erosion
argument: ``-dilate``, position: 1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
the output image
GenerateWhiteMatterMask¶
Wraps the executable command gen_WM_mask
.
Generates a white matter probability mask from the DW images.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> genWM = mrt.GenerateWhiteMatterMask()
>>> genWM.inputs.in_file = 'dwi.mif'
>>> genWM.inputs.encoding_file = 'encoding.txt'
>>> genWM.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Diffusion-weighted images
argument: ``%s``, position: -3
binary_mask: (a pathlike object or string representing an existing
file)
Binary brain mask
argument: ``%s``, position: -2
encoding_file: (a pathlike object or string representing an existing
file)
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
argument: ``-grad %s``, position: 1
[Optional]
out_WMProb_filename: (a pathlike object or string representing a
file)
Output WM probability image filename
argument: ``%s``, position: -1
noise_level_margin: (a float)
Specify the width of the margin on either side of the image to be
used to estimate the noise level (default = 10)
argument: ``-margin %s``
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
WMprobabilitymap: (a pathlike object or string representing an
existing file)
WMprobabilitymap
MRConvert¶
Wraps the executable command mrconvert
.
Perform conversion between different file types and optionally extract a subset of the input image.
If used correctly, this program can be a very useful workhorse. In addition to converting images between different formats, it can be used to extract specific studies from a data set, extract a specific region of interest, flip the images, or to scale the intensity of the images.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> mrconvert = mrt.MRConvert()
>>> mrconvert.inputs.in_file = 'dwi_FA.mif'
>>> mrconvert.inputs.out_filename = 'dwi_FA.nii'
>>> mrconvert.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
voxel-order data filename
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output filename
argument: ``%s``, position: -1
extract_at_axis: (1 or 2 or 3)
"Extract data only at the coordinates specified. This option
specifies the Axis. Must be used in conjunction with
extract_at_coordinate.
argument: ``-coord %s``, position: 1
extract_at_coordinate: (a list of from 1 to 3 items which are a
float)
"Extract data only at the coordinates specified. This option
specifies the coordinates. Must be used in conjunction with
extract_at_axis. Three comma-separated numbers giving the size of
each voxel in mm.
argument: ``%s``, position: 2
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.
argument: ``-vox %s``, position: 3
output_datatype: ('nii' or 'float' or 'char' or 'short' or 'int' or
'long' or 'double')
"i.e. Bfloat". Can be "char", "short", "int", "long", "float" or
"double"
argument: ``-output %s``, position: 2
extension: ('mif' or 'nii' or 'float' or 'char' or 'short' or 'int'
or 'long' or 'double', nipype default value: mif)
"i.e. Bfloat". Can be "char", "short", "int", "long", "float" or
"double"
layout: ('nii' or 'float' or 'char' or 'short' or 'int' or 'long' or
'double')
specify the layout of the data in memory. The actual layout produced
will depend on whether the output image format can support it.
argument: ``-output %s``, position: 2
resample: (a float)
Apply scaling to the intensity values.
argument: ``-scale %d``, position: 3
offset_bias: (a float)
Apply offset to the intensity values.
argument: ``-scale %d``, position: 3
replace_NaN_with_zero: (a boolean)
Replace all NaN values with zero.
argument: ``-zero``, position: 3
prs: (a boolean)
Assume that the DW gradients are specified in the PRS frame (Siemens
DICOM only).
argument: ``-prs``, position: 3
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
converted: (a pathlike object or string representing an existing
file)
path/name of 4D volume in voxel order
MRMultiply¶
Wraps the executable command mrmult
.
Multiplies two images.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> MRmult = mrt.MRMultiply()
>>> MRmult.inputs.in_files = ['dwi.mif', 'dwi_WMProb.mif']
>>> MRmult.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
Input images to be multiplied
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output image filename
argument: ``%s``, position: -1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
the output image of the multiplication
MRTransform¶
Wraps the executable command mrtransform
.
Apply spatial transformations or reslice images
Example¶
>>> MRxform = MRTransform()
>>> MRxform.inputs.in_files = 'anat_coreg.mif'
>>> MRxform.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
Input images to be transformed
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output image
argument: ``%s``, position: -1
invert: (a boolean)
Invert the specified transform before using it
argument: ``-inverse``, position: 1
replace_transform: (a boolean)
replace the current transform by that specified, rather than
applying it to the current transform
argument: ``-replace``, position: 1
transformation_file: (a pathlike object or string representing an
existing file)
The transform to apply, in the form of a 4x4 ascii file.
argument: ``-transform %s``, position: 1
template_image: (a pathlike object or string representing an existing
file)
Reslice the input image to match the specified template image.
argument: ``-template %s``, position: 1
reference_image: (a pathlike object or string representing an
existing file)
in case the transform supplied maps from the input image onto a
reference image, use this option to specify the reference. Note that
this implicitly sets the -replace option.
argument: ``-reference %s``, position: 1
flip_x: (a boolean)
assume the transform is supplied assuming a coordinate system with
the x-axis reversed relative to the MRtrix convention (i.e. x
increases from right to left). This is required to handle transform
matrices produced by FSL's FLIRT command. This is only used in
conjunction with the -reference option.
argument: ``-flipx``, position: 1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
the output image of the transformation
MRTrixViewer¶
Wraps the executable command mrview
.
Loads the input images in the MRTrix Viewer.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> MRview = mrt.MRTrixViewer()
>>> MRview.inputs.in_files = 'dwi.mif'
>>> MRview.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_files: (a list of items which are a pathlike object or string
representing an existing file)
Input images to be viewed
argument: ``%s``, position: -2
[Optional]
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
None
MedianFilter3D¶
Wraps the executable command median3D
.
Smooth images using a 3x3x3 median filter.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> median3d = mrt.MedianFilter3D()
>>> median3d.inputs.in_file = 'mask.mif'
>>> median3d.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Input images to be smoothed
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output image filename
argument: ``%s``, position: -1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
the output image
Tensor2ApparentDiffusion¶
Wraps the executable command tensor2ADC
.
Generates a map of the apparent diffusion coefficient (ADC) in each voxel
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2ADC = mrt.Tensor2ApparentDiffusion()
>>> tensor2ADC.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2ADC.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Diffusion tensor image
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output Fractional Anisotropy filename
argument: ``%s``, position: -1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
ADC: (a pathlike object or string representing an existing file)
the output image of the major eigenvectors of the diffusion tensor
image.
Tensor2FractionalAnisotropy¶
Wraps the executable command tensor2FA
.
Generates a map of the fractional anisotropy in each voxel.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2FA = mrt.Tensor2FractionalAnisotropy()
>>> tensor2FA.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2FA.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Diffusion tensor image
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output Fractional Anisotropy filename
argument: ``%s``, position: -1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
FA: (a pathlike object or string representing an existing file)
the output image of the major eigenvectors of the diffusion tensor
image.
Tensor2Vector¶
Wraps the executable command tensor2vector
.
Generates a map of the major eigenvectors of the tensors in each voxel.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2vector = mrt.Tensor2Vector()
>>> tensor2vector.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2vector.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
Diffusion tensor image
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
Output vector filename
argument: ``%s``, position: -1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
vector: (a pathlike object or string representing an existing file)
the output image of the major eigenvectors of the diffusion tensor
image.
Threshold¶
Wraps the executable command threshold
.
Create bitwise image by thresholding image intensity.
By default, the threshold level is determined using a histogram analysis to cut out the background. Otherwise, the threshold intensity can be specified using command line options. Note that only the first study is used for thresholding.
Example¶
>>> import nipype.interfaces.mrtrix as mrt
>>> thresh = mrt.Threshold()
>>> thresh.inputs.in_file = 'wm_mask.mif'
>>> thresh.run() # doctest: +SKIP
Inputs:
[Mandatory]
in_file: (a pathlike object or string representing an existing file)
The input image to be thresholded
argument: ``%s``, position: -2
[Optional]
out_filename: (a pathlike object or string representing a file)
The output binary image mask.
argument: ``%s``, position: -1
absolute_threshold_value: (a float)
Specify threshold value as absolute intensity.
argument: ``-abs %s``
percentage_threshold_value: (a float)
Specify threshold value as a percentage of the peak intensity in the
input image.
argument: ``-percent %s``
invert: (a boolean)
Invert output binary mask
argument: ``-invert``, position: 1
replace_zeros_with_NaN: (a boolean)
Replace all zero values with NaN
argument: ``-nan``, position: 1
quiet: (a boolean)
Do not display information messages or progress status.
argument: ``-quiet``, position: 1
debug: (a boolean)
Display debugging messages.
argument: ``-debug``, position: 1
args: (a unicode string)
Additional parameters to the command
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', nipype default value: {})
Environment variables
Outputs:
out_file: (a pathlike object or string representing an existing file)
The output binary image mask.