Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L99
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L406
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L362
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L43
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L264
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L546
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L304
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L496
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
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L223
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.
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L182
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.
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L141
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.
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/mrtrix/preprocess.py#L450
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.