Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/utilities.py#L83
Wraps command ** DiffusionTensorEstimation **
There are three estimation methods available: least squares, weigthed least squares and non-linear estimation. The first method is the traditional method for tensor estimation and the fastest one. Weighted least squares takes into account the noise characteristics of the MRI images to weight the DWI samples used in the estimation based on its intensity magnitude. The last method is the more complex.
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DiffusionTensorEstimation
license: slicer3
contributor: Raul San Jose
acknowledgements: This command module is based on the estimation functionality provided by the Teem library. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('LS' or 'WLS')
LS: Least Squares, WLS: Weighted Least Squares
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DWI volume
mask: (an existing file name)
Mask where the tensors will be computed
outputBaseline: (a boolean or a file name)
Estimated baseline volume
outputTensor: (a boolean or a file name)
Estimated DTI volume
shiftNeg: (a boolean)
Shift eigenvalues so all are positive (accounts for bad tensors related to noise or
acquisition error)
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
outputTensor: (an existing file name)
Estimated DTI volume
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/utilities.py#L166
Wraps command ** DiffusionTensorMathematics **
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DiffusionTensorMathematics
contributor: Raul San Jose
acknowledgements: LMI
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('Trace' or 'Determinant' or 'RelativeAnisotropy' or 'FractionalAnisotropy'
or 'Mode' or 'LinearMeasure' or 'PlanarMeasure' or 'SphericalMeasure' or
'MinEigenvalue' or 'MidEigenvalue' or 'MaxEigenvalue' or 'MaxEigenvalueProjectionX' or
'MaxEigenvalueProjectionY' or 'MaxEigenvalueProjectionZ' or 'RAIMaxEigenvecX' or
'RAIMaxEigenvecY' or 'RAIMaxEigenvecZ' or 'D11' or 'D22' or 'D33' or
'ParallelDiffusivity' or 'PerpendicularDffusivity')
An enumeration of strings
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DTI volume
outputScalar: (a boolean or a file name)
Scalar volume derived from tensor
Outputs:
outputScalar: (an existing file name)
Scalar volume derived from tensor
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/utilities.py#L129
Wraps command ** DiffusionWeightedMasking **
description: <p>Performs a mask calculation from a diffusion weighted (DW) image.</p><p>Starting from a dw image, this module computes the baseline image averaging all the images without diffusion weighting and then applies the otsu segmentation algorithm in order to produce a mask. this mask can then be used when estimating the diffusion tensor (dt) image, not to estimate tensors all over the volume.</p>
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DiffusionWeightedMasking
license: slicer3
contributor: Demian Wassermann
Inputs:
[Mandatory]
[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
inputVolume: (an existing file name)
Input DWI volume
otsuomegathreshold: (a float)
Control the sharpness of the threshold in the Otsu computation. 0: lower threshold, 1:
higher threhold
outputBaseline: (a boolean or a file name)
Estimated baseline volume
removeislands: (a boolean)
Remove Islands in Threshold Mask?
thresholdMask: (a boolean or a file name)
Otsu Threshold Mask
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
thresholdMask: (an existing file name)
Otsu Threshold Mask
Code: file:///build/buildd/nipype-0.6.0/nipype/interfaces/slicer/diffusion/utilities.py#L42
Wraps command ** ResampleDTI **
title: Resample DTI Volume
category: Diffusion.Utilities
description: Resampling an image is a very important task in image analysis. It is especially important in the frame of image registration. This module implements DT image resampling through the use of itk Transforms. The resampling is controlled by the Output Spacing. “Resampling” is performed in space coordinates, not pixel/grid coordinates. It is quite important to ensure that image spacing is properly set on the images involved. The interpolator is required since the mapping from one space to the other will often require evaluation of the intensity of the image at non-grid positions.
version: 0.1
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/ResampleDTI
contributor: Francois Budin
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics
Inputs:
[Mandatory]
[Optional]
Inverse_ITK_Transformation: (a boolean)
Inverse the transformation before applying it from output image to input image (only for
rigid and affine transforms)
Reference: (an existing file name)
Reference Volume (spacing,size,orientation,origin)
args: (a string)
Additional parameters to the command
centered_transform: (a boolean)
Set the center of the transformation to the center of the input image (only for rigid
and affine transforms)
correction: ('zero' or 'none' or 'abs' or 'nearest')
Correct the tensors if computed tensor is not semi-definite positive
defField: (an existing file name)
File containing the deformation field (3D vector image containing vectors with 3
components)
default_pixel_value: (a float)
Default pixel value for samples falling outside of the input region
direction_matrix: (a float)
9 parameters of the direction matrix by rows (ijk to LPS if LPS transform, ijk to RAS if
RAS transform)
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
hfieldtype: ('displacement' or 'h-Field')
Set if the deformation field is an -Field
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
image_center: ('input' or 'output')
Image to use to center the transform (used only if "Centered Transform" is selected)
inputVolume: (an existing file name)
Input volume to be resampled
interpolation: ('linear' or 'nn' or 'ws' or 'bs')
Sampling algorithm (linear , nn (nearest neighborhoor), ws (WindowedSinc), bs (BSpline)
~
notbulk: (a boolean)
The transform following the BSpline transform is not set as a bulk transform for the
BSpline transform
number_of_thread: (an integer)
Number of thread used to compute the output image
origin: (a list of items which are any value)
Origin of the output Image
outputVolume: (a boolean or a file name)
Resampled Volume
rotation_point: (a list of items which are any value)
Center of rotation (only for rigid and affine transforms)
size: (a float)
Size along each dimension (0 means use input size)
spaceChange: (a boolean)
Space Orientation between transform and image is different (RAS/LPS) (warning: if the
transform is a Transform Node in Slicer3, do not select)
spacing: (a float)
Spacing along each dimension (0 means use input spacing)
spline_order: (an integer)
Spline Order (Spline order may be from 0 to 5)
transform: ('rt' or 'a')
Transform algorithm, rt = Rigid Transform, a = Affine Transform
transform_matrix: (a float)
12 parameters of the transform matrix by rows ( --last 3 being translation-- )
transform_order: ('input-to-output' or 'output-to-input')
Select in what order the transforms are read
transform_tensor_method: ('PPD' or 'FS')
Chooses between 2 methods to transform the tensors: Finite Strain (FS), faster but less
accurate, or Preservation of the Principal Direction (PPD)
transformationFile: (an existing file name)
window_function: ('h' or 'c' or 'w' or 'l' or 'b')
Window Function , h = Hamming , c = Cosine , w = Welch , l = Lanczos , b = Blackman
Outputs:
outputVolume: (an existing file name)
Resampled Volume