Wraps command dwi2tensor
Converts diffusion-weighted images to tensor images.
>>> import nipype.interfaces.mrtrix as mrt
>>> dwi2tensor = mrt.DWI2Tensor()
>>> dwi2tensor.inputs.in_file = 'dwi.mif'
>>> dwi2tensor.inputs.encoding_file = 'encoding.txt'
>>> dwi2tensor.run()
Inputs:
[Mandatory]
in_file
Diffusion-weighted images
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
encoding_file: (a file name)
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()
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
ignore_slice_by_volume: (a list of from 2 to 2 items which are an integer)
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.
ignore_volumes: (a list of at least 1 items which are an integer)
Requires two values (i.e. [2 5 6] for [Volumes] Ignores the image volumes specified when
computing the tensor.
out_filename: (a file name)
Output tensor filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
tensor: (an existing file name)
path/name of output diffusion tensor image
Wraps command erode
Erode (or dilates) a mask (i.e. binary) image
>>> import nipype.interfaces.mrtrix as mrt
>>> erode = mrt.Erode()
>>> erode.inputs.in_file = 'mask.mif'
>>> erode.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Input mask image to be eroded
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
dilate: (a boolean)
Perform dilation rather than erosion
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
number_of_passes: (an integer)
the number of passes (default: 1)
out_filename: (a file name)
Output image filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
out_file: (an existing file name)
the output image
Wraps command gen_WM_mask
Generates a white matter probability mask from the DW images.
>>> import nipype.interfaces.mrtrix as mrt
>>> genWM = mrt.GenerateWhiteMatterMask()
>>> genWM.inputs.in_file = 'dwi.mif'
>>> genWM.inputs.encoding_file = 'encoding.txt'
>>> genWM.run()
Inputs:
[Mandatory]
binary_mask: (an existing file name)
Binary brain mask
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)
Diffusion-weighted images
[Optional]
args: (a string)
Additional parameters to the command
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
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)
out_WMProb_filename: (a file name)
Output WM probability image filename
Outputs:
WMprobabilitymap: (an existing file name)
WMprobabilitymap
Wraps 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.
>>> 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()
Inputs:
[Mandatory]
in_file: (an existing file name)
voxel-order data filename
[Optional]
args: (a string)
Additional parameters to the command
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
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"
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.
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.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
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.
offset_bias: (a float)
Apply offset to the intensity values.
out_filename: (a file name)
Output filename
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"
prs: (a boolean)
Assume that the DW gradients are specified in the PRS frame (Siemens DICOM only).
replace_NaN_with_zero: (a boolean)
Replace all NaN values with zero.
resample: (a float)
Apply scaling to the intensity values.
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:
converted: (an existing file name)
path/name of 4D volume in voxel order
Wraps command mrmult
Multiplies two images.
>>> import nipype.interfaces.mrtrix as mrt
>>> MRmult = mrt.MRMultiply()
>>> MRmult.inputs.in_files = ['dwi.mif', 'dwi_WMProb.mif']
>>> MRmult.run()
Inputs:
[Mandatory]
in_files
Input images to be multiplied
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_filename: (a file name)
Output image filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
out_file: (an existing file name)
the output image of the multiplication
Wraps command mrtransform
Apply spatial transformations or reslice images
>>> MRxform = MRTransform()
>>> MRxform.inputs.in_files = 'anat_coreg.mif'
>>> MRxform.run()
Inputs:
[Mandatory]
in_files
Input images to be transformed
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
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.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
invert: (a boolean)
Invert the specified transform before using it
out_filename: (a file name)
Output image
quiet: (a boolean)
Do not display information messages or progress status.
reference_image: (an existing file name)
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.
replace_transform: (a boolean)
replace the current transform by that specified, rather than applying it to the current
transform
template_image: (an existing file name)
Reslice the input image to match the specified template image.
transformation_file: (an existing file name)
The transform to apply, in the form of a 4x4 ascii file.
Outputs:
out_file: (an existing file name)
the output image of the transformation
Wraps command mrview
Loads the input images in the MRTrix Viewer.
>>> import nipype.interfaces.mrtrix as mrt
>>> MRview = mrt.MRTrixViewer()
>>> MRview.inputs.in_files = 'dwi.mif'
>>> MRview.run()
Inputs:
[Mandatory]
in_files
Input images to be viewed
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
None
Wraps command median3D
Smooth images using a 3x3x3 median filter.
>>> import nipype.interfaces.mrtrix as mrt
>>> median3d = mrt.MedianFilter3D()
>>> median3d.inputs.in_file = 'mask.mif'
>>> median3d.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Input images to be smoothed
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_filename: (a file name)
Output image filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
out_file: (an existing file name)
the output image
Wraps command tensor2ADC
Generates a map of the apparent diffusion coefficient (ADC) in each voxel
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2ADC = mrt.Tensor2ApparentDiffusion()
>>> tensor2ADC.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2ADC.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Diffusion tensor image
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_filename: (a file name)
Output Fractional Anisotropy filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
ADC: (an existing file name)
the output image of the major eigenvectors of the diffusion tensor image.
Wraps command tensor2FA
Generates a map of the fractional anisotropy in each voxel.
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2FA = mrt.Tensor2FractionalAnisotropy()
>>> tensor2FA.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2FA.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Diffusion tensor image
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_filename: (a file name)
Output Fractional Anisotropy filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
FA: (an existing file name)
the output image of the major eigenvectors of the diffusion tensor image.
Wraps command tensor2vector
Generates a map of the major eigenvectors of the tensors in each voxel.
>>> import nipype.interfaces.mrtrix as mrt
>>> tensor2vector = mrt.Tensor2Vector()
>>> tensor2vector.inputs.in_file = 'dwi_tensor.mif'
>>> tensor2vector.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Diffusion tensor image
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_filename: (a file name)
Output vector filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
vector: (an existing file name)
the output image of the major eigenvectors of the diffusion tensor image.
Wraps 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.
>>> import nipype.interfaces.mrtrix as mrt
>>> thresh = mrt.Threshold()
>>> thresh.inputs.in_file = 'wm_mask.mif'
>>> thresh.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
The input image to be thresholded
[Optional]
absolute_threshold_value: (a float)
Specify threshold value as absolute intensity.
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
invert: (a boolean)
Invert output binary mask
out_filename: (a file name)
The output binary image mask.
percentage_threshold_value: (a float)
Specify threshold value as a percentage of the peak intensity in the input image.
quiet: (a boolean)
Do not display information messages or progress status.
replace_zeros_with_NaN: (a boolean)
Replace all zero values with NaN
Outputs:
out_file: (an existing file name)
The output binary image mask.