Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L16
Wraps command ** Add **
title: Add Images
category: Filtering.Arithmetic
description: Adds two images. Although all image types are supported on input, only signed types are produced. The two images do not have to have the same dimensions.
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/Add
contributor: Bill Lorensen
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume1: (an existing file name)
Input volume 1
inputVolume2: (an existing file name)
Input volume 2
order: ('0' or '1' or '2' or '3')
Interpolation order if two images are in different coordinate frames or have different
sampling.
outputVolume: (a boolean or a file name)
Volume1 + Volume2
Outputs:
outputVolume: (an existing file name)
Volume1 + Volume2
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L86
Wraps command ** AffineRegistration **
title: Fast Affine registration
category: Legacy.Registration
description: Registers two images together using an affine transform and mutual information. This module is often used to align images of different subjects or images of the same subject from different modalities.
This module can smooth images prior to registration to mitigate noise and improve convergence. Many of the registration parameters require a working knowledge of the algorithm although the default parameters are sufficient for many registration tasks.
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/AffineRegistration
contributor: Daniel Blezek
acknowledgements: This module was developed by Daniel Blezek while at GE Research with contributions from Jim Miller.
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]
FixedImageFileName: (an existing file name)
Fixed image to which to register
MovingImageFileName: (an existing file name)
Moving image
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
fixedsmoothingfactor: (an integer)
Amount of smoothing applied to fixed image prior to registration. Default is 0 (none).
Range is 0-5 (unitless). Consider smoothing the input data if there is considerable
amounts of noise or the noise pattern in the fixed and moving images is very different.
histogrambins: (an integer)
Number of histogram bins to use for Mattes Mutual Information. Reduce the number of bins
if a registration fails. If the number of bins is too large, the estimated PDFs will be
a field of impulses and will inhibit reliable registration estimation.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
initialtransform: (an existing file name)
Initial transform for aligning the fixed and moving image. Maps positions in the fixed
coordinate frame to positions in the moving coordinate frame. Optional.
iterations: (an integer)
Number of iterations
movingsmoothingfactor: (an integer)
Amount of smoothing applied to moving image prior to registration. Default is 0 (none).
Range is 0-5 (unitless). Consider smoothing the input data if there is considerable
amounts of noise or the noise pattern in the fixed and moving images is very different.
outputtransform: (a boolean or a file name)
Transform calculated that aligns the fixed and moving image. Maps positions in the fixed
coordinate frame to the moving coordinate frame. Optional (specify an output transform
or an output volume or both).
resampledmovingfilename: (a boolean or a file name)
Resampled moving image to the fixed image coordinate frame. Optional (specify an output
transform or an output volume or both).
spatialsamples: (an integer)
Number of spatial samples to use in estimating Mattes Mutual Information. Larger values
yield more accurate PDFs and improved registration quality.
translationscale: (a float)
Relative scale of translations to rotations, i.e. a value of 100 means 10mm = 1 degree.
(Actual scale used is 1/(TranslationScale^2)). This parameter is used to "weight" or
"standardized" the transform parameters and their effect on the registration objective
function.
Outputs:
outputtransform: (an existing file name)
Transform calculated that aligns the fixed and moving image. Maps positions in the fixed
coordinate frame to the moving coordinate frame. Optional (specify an output transform
or an output volume or both).
resampledmovingfilename: (an existing file name)
Resampled moving image to the fixed image coordinate frame. Optional (specify an output
transform or an output volume or both).
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1321
Wraps command ** BRAINSDemonWarp **
title: Demon Registration (BRAINS)
category: Registration
version: 3.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:BRAINSDemonWarp
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: This tool was developed by Hans J. Johnson and Greg Harris.
acknowledgements: The development of this tool was supported by funding from grants NS050568 and NS40068 from the National Institute of Neurological Disorders and Stroke and grants MH31593, MH40856, from the National Institute of Mental Health.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
arrayOfPyramidLevelIterations: (an integer)
The number of iterations for each pyramid level
backgroundFillValue: (an integer)
Replacement value to overwrite background when performing BOBF
checkerboardPatternSubdivisions: (an integer)
Number of Checkerboard subdivisions in all 3 directions
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
fixedBinaryVolume: (an existing file name)
Mask filename for desired region of interest in the Fixed image.
fixedVolume: (an existing file name)
Required: input fixed (target) image
gradient_type: ('0' or '1' or '2')
Type of gradient used for computing the demons force (0 is symmetrized, 1 is fixed
image, 2 is moving image)
gui: (a boolean)
Display intermediate image volumes for debugging
histogramMatch: (a boolean)
Histogram Match the input images. This is suitable for images of the same modality that
may have different absolute scales, but the same overall intensity profile.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
initializeWithDeformationField: (an existing file name)
Initial deformation field vector image file name
initializeWithTransform: (an existing file name)
Initial Transform filename
inputPixelType: ('float' or 'short' or 'ushort' or 'int' or 'uchar')
Input volumes will be typecast to this format: float|short|ushort|int|uchar
interpolationMode: ('NearestNeighbor' or 'Linear' or 'ResampleInPlace' or 'BSpline' or
'WindowedSinc' or 'Hamming' or 'Cosine' or 'Welch' or 'Lanczos' or 'Blackman')
Type of interpolation to be used when applying transform to moving volume. Options are
Linear, ResampleInPlace, NearestNeighbor, BSpline, or WindowedSinc
lowerThresholdForBOBF: (an integer)
Lower threshold for performing BOBF
maskProcessingMode: ('NOMASK' or 'ROIAUTO' or 'ROI' or 'BOBF')
What mode to use for using the masks: NOMASK|ROIAUTO|ROI|BOBF. If ROIAUTO is choosen,
then the mask is implicitly defined using a otsu forground and hole filling algorithm.
Where the Region Of Interest mode uses the masks to define what parts of the image
should be used for computing the deformation field. Brain Only Background Fill uses the
masks to pre-process the input images by clipping and filling in the background with a
predefined value.
max_step_length: (a float)
Maximum length of an update vector (0: no restriction)
medianFilterSize: (an integer)
Median filter radius in all 3 directions. When images have a lot of salt and pepper
noise, this step can improve the registration.
minimumFixedPyramid: (an integer)
The shrink factor for the first level of the fixed image pyramid. (i.e. start at 1/16
scale, then 1/8, then 1/4, then 1/2, and finally full scale)
minimumMovingPyramid: (an integer)
The shrink factor for the first level of the moving image pyramid. (i.e. start at 1/16
scale, then 1/8, then 1/4, then 1/2, and finally full scale)
movingBinaryVolume: (an existing file name)
Mask filename for desired region of interest in the Moving image.
movingVolume: (an existing file name)
Required: input moving image
neighborhoodForBOBF: (an integer)
neighborhood in all 3 directions to be included when performing BOBF
numberOfBCHApproximationTerms: (an integer)
Number of terms in the BCH expansion
numberOfHistogramBins: (an integer)
The number of histogram levels
numberOfMatchPoints: (an integer)
The number of match points for histrogramMatch
numberOfPyramidLevels: (an integer)
Number of image pyramid levels to use in the multi-resolution registration.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputCheckerboardVolume: (a boolean or a file name)
Genete a checkerboard image volume between the fixedVolume and the deformed
movingVolume.
outputDebug: (a boolean)
Flag to write debugging images after each step.
outputDeformationFieldVolume: (a boolean or a file name)
Output deformation field vector image (will have the same physical space as the
fixedVolume).
outputDisplacementFieldPrefix: (a string)
Displacement field filename prefix for writing separate x, y, and z component images
outputNormalized: (a boolean)
Flag to warp and write the normalized images to output. In normalized images the image
values are fit-scaled to be between 0 and the maximum storage type value.
outputPixelType: ('float' or 'short' or 'ushort' or 'int' or 'uchar')
outputVolume will be typecast to this format: float|short|ushort|int|uchar
outputVolume: (a boolean or a file name)
Required: output resampled moving image (will have the same physical space as the
fixedVolume).
promptUser: (a boolean)
Prompt the user to hit enter each time an image is sent to the DebugImageViewer
registrationFilterType: ('Demons' or 'FastSymmetricForces' or 'Diffeomorphic' or
'LogDemons' or 'SymmetricLogDemons')
Registration Filter Type:
Demons|FastSymmetricForces|Diffeomorphic|LogDemons|SymmetricLogDemons
seedForBOBF: (an integer)
coordinates in all 3 directions for Seed when performing BOBF
smoothDeformationFieldSigma: (a float)
A gaussian smoothing value to be applied to the deformation feild at each iteration.
upFieldSmoothing: (a float)
Smoothing sigma for the update field at each iteration
upperThresholdForBOBF: (an integer)
Upper threshold for performing BOBF
use_vanilla_dem: (a boolean)
Run vanilla demons algorithm
Outputs:
outputCheckerboardVolume: (an existing file name)
Genete a checkerboard image volume between the fixedVolume and the deformed
movingVolume.
outputDeformationFieldVolume: (an existing file name)
Output deformation field vector image (will have the same physical space as the
fixedVolume).
outputVolume: (an existing file name)
Required: output resampled moving image (will have the same physical space as the
fixedVolume).
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1223
Wraps command ** BRAINSFit **
title: General Registration (BRAINS)
category: Registration
description: Register a three-dimensional volume to a reference volume (Mattes Mutual Information by default). Full documentation avalable here: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/BRAINSFit. Method described in BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit, Johnson H.J., Harris G., Williams K., The Insight Journal, 2007. http://hdl.handle.net/1926/1291
version: 3.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:BRAINSFit
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: Hans J. Johnson, hans-johnson -at- uiowa.edu, http://wwww.psychiatry.uiowa.edu
acknowledgements: Hans Johnson(1,3,4); Kent Williams(1); Gregory Harris(1), Vincent Magnotta(1,2,3); Andriy Fedorov(5) 1=University of Iowa Department of Psychiatry, 2=University of Iowa Department of Radiology, 3=University of Iowa Department of Biomedical Engineering, 4=University of Iowa Department of Electrical and Computer Engineering, 5=Surgical Planning Lab, Harvard
Inputs:
[Mandatory]
[Optional]
NEVER_USE_THIS_FLAG_IT_IS_OUTDATED_00: (a boolean)
DO NOT USE THIS FLAG
NEVER_USE_THIS_FLAG_IT_IS_OUTDATED_01: (a boolean)
DO NOT USE THIS FLAG
NEVER_USE_THIS_FLAG_IT_IS_OUTDATED_02: (a boolean)
DO NOT USE THIS FLAG
ROIAutoClosingSize: (a float)
This flag is only relavent when using ROIAUTO mode for initializing masks. It defines
the hole closing size in mm. It is rounded up to the nearest whole pixel size in each
direction. The default is to use a closing size of 9mm. For mouse data this value may
need to be reset to 0.9 or smaller.
ROIAutoDilateSize: (a float)
This flag is only relavent when using ROIAUTO mode for initializing masks. It defines
the final dilation size to capture a bit of background outside the tissue region. At
setting of 10mm has been shown to help regularize a BSpline registration type so that
there is some background constraints to match the edges of the head better.
args: (a string)
Additional parameters to the command
backgroundFillValue: (a float)
Background fill value for output image.
bsplineTransform: (a boolean or a file name)
(optional) Filename to which save the estimated transform. NOTE: You must set at least
one output object (either a deformed image or a transform. NOTE: USE THIS ONLY IF THE
FINAL TRANSFORM IS BSpline
costFunctionConvergenceFactor: (a float)
From itkLBFGSBOptimizer.h: Set/Get the CostFunctionConvergenceFactor. Algorithm
terminates when the reduction in cost function is less than (factor * epsmcj) where
epsmch is the machine precision. Typical values for factor: 1e+12 for low accuracy; 1e+7
for moderate accuracy and 1e+1 for extremely high accuracy. 1e+9 seems to work well.,
costMetric: ('MMI' or 'MSE' or 'NC' or 'MC')
The cost metric to be used during fitting. Defaults to MMI. Options are MMI (Mattes
Mutual Information), MSE (Mean Square Error), NC (Normalized Correlation), MC (Match
Cardinality for binary images)
debugLevel: (an integer)
Display debug messages, and produce debug intermediate results. 0=OFF, 1=Minimal,
10=Maximum debugging.
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
failureExitCode: (an integer)
If the fit fails, exit with this status code. (It can be used to force a successfult
exit status of (0) if the registration fails due to reaching the maximum number of
iterations.
fixedBinaryVolume: (an existing file name)
Fixed Image binary mask volume, ONLY FOR MANUAL ROI mode.
fixedVolume: (an existing file name)
The fixed image for registration by mutual information optimization.
fixedVolumeTimeIndex: (an integer)
The index in the time series for the 3D fixed image to fit, if 4-dimensional.
forceMINumberOfThreads: (an integer)
Force the the maximum number of threads to use for non thread safe MI metric.
gui: (a boolean)
Display intermediate image volumes for debugging. NOTE: This is not part of the
standard build sytem, and probably does nothing on your installation.
histogramMatch: (a boolean)
Histogram Match the input images. This is suitable for images of the same modality that
may have different absolute scales, but the same overall intensity profile. Do NOT use
if registering images from different modailties.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
initialTransform: (an existing file name)
Filename of transform used to initialize the registration. This CAN NOT be used with
either CenterOfHeadLAlign, MomentsAlign, GeometryAlign, or initialTransform file.
initializeTransformMode: ('Off' or 'useMomentsAlign' or 'useCenterOfHeadAlign' or
'useGeometryAlign' or 'useCenterOfROIAlign')
Determine how to initialize the transform center. GeometryAlign on assumes that the
center of the voxel lattice of the images represent similar structures. MomentsAlign
assumes that the center of mass of the images represent similar structures.
useCenterOfHeadAlign attempts to use the top of head and shape of neck to drive a center
of mass estimate. Off assumes that the physical space of the images are close, and that
centering in terms of the image Origins is a good starting point. This flag is mutually
exclusive with the initialTransform flag.
interpolationMode: ('NearestNeighbor' or 'Linear' or 'ResampleInPlace' or 'BSpline' or
'WindowedSinc' or 'Hamming' or 'Cosine' or 'Welch' or 'Lanczos' or 'Blackman')
Type of interpolation to be used when applying transform to moving volume. Options are
Linear, NearestNeighbor, BSpline, WindowedSinc, or ResampleInPlace. The ResampleInPlace
option will create an image with the same discrete voxel values and will adjust the
origin and direction of the physical space interpretation.
linearTransform: (a boolean or a file name)
(optional) Filename to which save the estimated transform. NOTE: You must set at least
one output object (either a deformed image or a transform. NOTE: USE THIS ONLY IF THE
FINAL TRANSFORM IS ---NOT--- BSpline
maskInferiorCutOffFromCenter: (a float)
For use with --useCenterOfHeadAlign (and --maskProcessingMode ROIAUTO): the cut-off
below the image centers, in millimeters,
maskProcessingMode: ('NOMASK' or 'ROIAUTO' or 'ROI')
What mode to use for using the masks. If ROIAUTO is choosen, then the mask is
implicitly defined using a otsu forground and hole filling algorithm. The Region Of
Interest mode (choose ROI) uses the masks to define what parts of the image should be
used for computing the transform.
maxBSplineDisplacement: (a float)
Sets the maximum allowed displacements in image physical coordinates for BSpline
control grid along each axis. A value of 0.0 indicates that the problem should be
unbounded. NOTE: This only constrains the BSpline portion, and does not limit the
displacement from the associated bulk transform. This can lead to a substantial
reduction in computation time in the BSpline optimizer.,
maximumStepLength: (a float)
Internal debugging parameter, and should probably never be used from the command line.
This will be removed in the future.
medianFilterSize: (an integer)
The radius for the optional MedianImageFilter preprocessing in all 3 directions.
minimumStepLength: (a float)
Each step in the optimization takes steps at least this big. When none are possible,
registration is complete.
movingBinaryVolume: (an existing file name)
Moving Image binary mask volume, ONLY FOR MANUAL ROI mode.
movingVolume: (an existing file name)
The moving image for registration by mutual information optimization.
movingVolumeTimeIndex: (an integer)
The index in the time series for the 3D moving image to fit, if 4-dimensional.
numberOfHistogramBins: (an integer)
The number of histogram levels
numberOfIterations: (an integer)
The maximum number of iterations to try before failing to converge. Use an explicit
limit like 500 or 1000 to manage risk of divergence
numberOfMatchPoints: (an integer)
the number of match points
numberOfSamples: (an integer)
The number of voxels sampled for mutual information computation. Increase this for a
slower, more careful fit. You can also limit the sampling focus with ROI masks and
ROIAUTO mask generation.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use. (default is auto-detected)
outputFixedVolumeROI: (a boolean or a file name)
The ROI automatically found in fixed image, ONLY FOR ROIAUTO mode.
outputMovingVolumeROI: (a boolean or a file name)
The ROI automatically found in moving image, ONLY FOR ROIAUTO mode.
outputTransform: (a boolean or a file name)
(optional) Filename to which save the (optional) estimated transform. NOTE: You must
select either the outputTransform or the outputVolume option.
outputVolume: (a boolean or a file name)
(optional) Output image for registration. NOTE: You must select either the
outputTransform or the outputVolume option.
outputVolumePixelType: ('float' or 'short' or 'ushort' or 'int' or 'uint' or 'uchar')
The output image Pixel Type is the scalar datatype for representation of the Output
Volume.
permitParameterVariation: (an integer)
A bit vector to permit linear transform parameters to vary under optimization. The
vector order corresponds with transform parameters, and beyond the end ones fill in as a
default. For instance, you can choose to rotate only in x (pitch) with 1,0,0; this is
mostly for expert use in turning on and off individual degrees of freedom in rotation,
translation or scaling without multiplying the number of transform representations; this
trick is probably meaningless when tried with the general affine transform.
projectedGradientTolerance: (a float)
From itkLBFGSBOptimizer.h: Set/Get the ProjectedGradientTolerance. Algorithm terminates
when the project gradient is below the tolerance. Default lbfgsb value is 1e-5, but 1e-4
seems to work well.,
promptUser: (a boolean)
Prompt the user to hit enter each time an image is sent to the DebugImageViewer
relaxationFactor: (a float)
Internal debugging parameter, and should probably never be used from the command line.
This will be removed in the future.
removeIntensityOutliers: (a float)
The half percentage to decide outliers of image intensities. The default value is zero,
which means no outlier removal. If the value of 0.005 is given, the moduel will throw
away 0.005 % of both tails, so 0.01% of intensities in total would be ignored in its
statistic calculation.
reproportionScale: (a float)
ScaleVersor3D 'Scale' compensation factor. Increase this to put more rescaling in a
ScaleVersor3D or ScaleSkewVersor3D search pattern. 1.0 works well with a
translationScale of 1000.0
scaleOutputValues: (a boolean)
If true, and the voxel values do not fit within the minimum and maximum values of the
desired outputVolumePixelType, then linearly scale the min/max output image voxel values
to fit within the min/max range of the outputVolumePixelType.
skewScale: (a float)
ScaleSkewVersor3D Skew compensation factor. Increase this to put more skew in a
ScaleSkewVersor3D search pattern. 1.0 works well with a translationScale of 1000.0
splineGridSize: (an integer)
The number of subdivisions of the BSpline Grid to be centered on the image space. Each
dimension must have at least 3 subdivisions for the BSpline to be correctly computed.
strippedOutputTransform: (a boolean or a file name)
File name for the rigid component of the estimated affine transform. Can be used to
rigidly register the moving image to the fixed image. NOTE: This value is overwritten
if either bsplineTransform or linearTransform is set.
transformType: (a string)
Specifies a list of registration types to be used. The valid types are, Rigid,
ScaleVersor3D, ScaleSkewVersor3D, Affine, and BSpline. Specifiying more than one in a
comma separated list will initialize the next stage with the previous results. If
registrationClass flag is used, it overrides this parameter setting.
translationScale: (a float)
How much to scale up changes in position compared to unit rotational changes in radians
-- decrease this to put more rotation in the search pattern.
useAffine: (a boolean)
Perform an Affine registration as part of the sequential registration steps. This
family of options superceeds the use of transformType if any of them are set.
useBSpline: (a boolean)
Perform a BSpline registration as part of the sequential registration steps. This
family of options superceeds the use of transformType if any of them are set.
useCachingOfBSplineWeightsMode: ('ON' or 'OFF')
This is a 5x speed advantage at the expense of requiring much more memory. Only
relevant when transformType is BSpline.
useComposite: (a boolean)
Perform a Composite registration as part of the sequential registration steps. This
family of options superceeds the use of transformType if any of them are set.
useExplicitPDFDerivativesMode: ('AUTO' or 'ON' or 'OFF')
Using mode AUTO means OFF for BSplineDeformableTransforms and ON for the linear
transforms. The ON alternative uses more memory to sometimes do a better job.
useRigid: (a boolean)
Perform a rigid registration as part of the sequential registration steps. This family
of options superceeds the use of transformType if any of them are set.
useScaleSkewVersor3D: (a boolean)
Perform a ScaleSkewVersor3D registration as part of the sequential registration steps.
This family of options superceeds the use of transformType if any of them are set.
useScaleVersor3D: (a boolean)
Perform a ScaleVersor3D registration as part of the sequential registration steps. This
family of options superceeds the use of transformType if any of them are set.
writeTransformOnFailure: (a boolean)
Flag to save the final transform even if the numberOfIterations are reached without
convergence. (Intended for use when --failureExitCode 0 )
Outputs:
bsplineTransform: (an existing file name)
(optional) Filename to which save the estimated transform. NOTE: You must set at least
one output object (either a deformed image or a transform. NOTE: USE THIS ONLY IF THE
FINAL TRANSFORM IS BSpline
linearTransform: (an existing file name)
(optional) Filename to which save the estimated transform. NOTE: You must set at least
one output object (either a deformed image or a transform. NOTE: USE THIS ONLY IF THE
FINAL TRANSFORM IS ---NOT--- BSpline
outputFixedVolumeROI: (an existing file name)
The ROI automatically found in fixed image, ONLY FOR ROIAUTO mode.
outputMovingVolumeROI: (an existing file name)
The ROI automatically found in moving image, ONLY FOR ROIAUTO mode.
outputTransform: (an existing file name)
(optional) Filename to which save the (optional) estimated transform. NOTE: You must
select either the outputTransform or the outputVolume option.
outputVolume: (an existing file name)
(optional) Output image for registration. NOTE: You must select either the
outputTransform or the outputVolume option.
strippedOutputTransform: (an existing file name)
File name for the rigid component of the estimated affine transform. Can be used to
rigidly register the moving image to the fixed image. NOTE: This value is overwritten
if either bsplineTransform or linearTransform is set.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1392
Wraps command ** BRAINSROIAuto **
title: Foreground masking (BRAINS)
category: Segmentation.Specialized
description: This program is used to create a mask over the most prominant forground region in an image. This is accomplished via a combination of otsu thresholding and a closing operation. More documentation is available here: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/ForegroundMasking.
version: 2.4.1
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: Hans J. Johnson, hans-johnson -at- uiowa.edu, http://wwww.psychiatry.uiowa.edu
acknowledgements: Hans Johnson(1,3,4); Kent Williams(1); Gregory Harris(1), Vincent Magnotta(1,2,3); Andriy Fedorov(5), fedorov -at- bwh.harvard.edu (Slicer integration); (1=University of Iowa Department of Psychiatry, 2=University of Iowa Department of Radiology, 3=University of Iowa Department of Biomedical Engineering, 4=University of Iowa Department of Electrical and Computer Engineering, 5=Surgical Planning Lab, Harvard)
Inputs:
[Mandatory]
[Optional]
ROIAutoDilateSize: (a float)
This flag is only relavent when using ROIAUTO mode for initializing masks. It defines
the final dilation size to capture a bit of background outside the tissue region. At
setting of 10mm has been shown to help regularize a BSpline registration type so that
there is some background constraints to match the edges of the head better.
args: (a string)
Additional parameters to the command
closingSize: (a float)
The Closing Size (in millimeters) for largest connected filled mask. This value is
divided by image spacing and rounded to the next largest voxel number.
cropOutput: (a boolean)
The inputVolume cropped to the region of the ROI mask.
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)
The input image for finding the largest region filled mask.
maskOutput: (a boolean)
The inputVolume multiplied by the ROI mask.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
otsuPercentileThreshold: (a float)
Parameter to the Otsu threshold algorithm.
outputROIMaskVolume: (a boolean or a file name)
The ROI automatically found from the input image.
outputVolume: (a boolean or a file name)
The inputVolume with optional [maskOutput|cropOutput] to the region of the brain mask.
outputVolumePixelType: ('float' or 'short' or 'ushort' or 'int' or 'uint' or 'uchar')
The output image Pixel Type is the scalar datatype for representation of the Output
Volume.
thresholdCorrectionFactor: (a float)
A factor to scale the Otsu algorithm's result threshold, in case clipping mangles the
image.
Outputs:
outputROIMaskVolume: (an existing file name)
The ROI automatically found from the input image.
outputVolume: (an existing file name)
The inputVolume with optional [maskOutput|cropOutput] to the region of the brain mask.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1458
Wraps command ** BRAINSResample **
title: Resample Image (BRAINS)
category: Registration
version: 3.0.0
documentation-url: http://www.slicer.org/slicerWiki/index.php/Modules:BRAINSResample
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: This tool was developed by Vincent Magnotta, Greg Harris, and Hans Johnson.
acknowledgements: The development of this tool was supported by funding from grants NS050568 and NS40068 from the National Institute of Neurological Disorders and Stroke and grants MH31593, MH40856, from the National Institute of Mental Health.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
defaultValue: (a float)
Default voxel value
deformationVolume: (an existing file name)
Displacement Field to be used to warp the image
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
gridSpacing: (an integer)
Add warped grid to output image to help show the deformation that occured with specified
spacing. A spacing of 0 in a dimension indicates that grid lines should be rendered to
fall exactly (i.e. do not allow displacements off that plane). This is useful for
makeing a 2D image of grid lines from the 3D space
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)
Image To Warp
interpolationMode: ('NearestNeighbor' or 'Linear' or 'ResampleInPlace' or 'BSpline' or
'WindowedSinc' or 'Hamming' or 'Cosine' or 'Welch' or 'Lanczos' or 'Blackman')
Type of interpolation to be used when applying transform to moving volume. Options are
Linear, ResampleInPlace, NearestNeighbor, BSpline, or WindowedSinc
inverseTransform: (a boolean)
True/False is to compute inverse of given transformation. Default is false
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Resulting deformed image
pixelType: ('float' or 'short' or 'ushort' or 'int' or 'uint' or 'uchar' or 'binary')
Specifies the pixel type for the input/output images. The "binary" pixel type uses a
modified algorithm whereby the image is read in as unsigned char, a signed distance map
is created, signed distance map is resampled, and then a thresholded image of type
unsigned char is written to disk.
referenceVolume: (an existing file name)
Reference image used only to define the output space. If not specified, the warping is
done in the same space as the image to warp.
warpTransform: (an existing file name)
Filename for the BRAINSFit transform used in place of the deformation field
Outputs:
outputVolume: (an existing file name)
Resulting deformed image
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L163
Wraps command ** BSplineDeformableRegistration **
title: Fast Nonrigid BSpline registration
category: Legacy.Registration
description: Registers two images together using BSpline transform and mutual information.
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/BSplineDeformableRegistration
contributor: Bill Lorensen
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.
Inputs:
[Mandatory]
[Optional]
FixedImageFileName: (an existing file name)
Fixed image to which to register
MovingImageFileName: (an existing file name)
Moving image
args: (a string)
Additional parameters to the command
constrain: (a boolean)
Constrain the deformation to the amount specified in Maximum Deformation
default: (an integer)
Default pixel value used if resampling a pixel outside of the volume.
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
gridSize: (an integer)
Number of grid points on interior of the fixed image. Larger grid sizes allow for finer
registrations.
histogrambins: (an integer)
Number of histogram bins to use for Mattes Mutual Information. Reduce the number of bins
if a deformable registration fails. If the number of bins is too large, the estimated
PDFs will be a field of impulses and will inhibit reliable registration estimation.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
initialtransform: (an existing file name)
Initial transform for aligning the fixed and moving image. Maps positions in the fixed
coordinate frame to positions in the moving coordinate frame. This transform should be
an affine or rigid transform. It is used an a bulk transform for the BSpline. Optional.
iterations: (an integer)
Number of iterations
maximumDeformation: (a float)
If Constrain Deformation is checked, limit the deformation to this amount.
outputtransform: (a boolean or a file name)
Transform calculated that aligns the fixed and moving image. Maps positions from the
fixed coordinate frame to the moving coordinate frame. Optional (specify an output
transform or an output volume or both).
outputwarp: (a boolean or a file name)
Vector field that applies an equivalent warp as the BSpline. Maps positions from the
fixed coordinate frame to the moving coordinate frame. Optional.
resampledmovingfilename: (a boolean or a file name)
Resampled moving image to fixed image coordinate frame. Optional (specify an output
transform or an output volume or both).
spatialsamples: (an integer)
Number of spatial samples to use in estimating Mattes Mutual Information. Larger values
yield more accurate PDFs and improved registration quality.
Outputs:
outputtransform: (an existing file name)
Transform calculated that aligns the fixed and moving image. Maps positions from the
fixed coordinate frame to the moving coordinate frame. Optional (specify an output
transform or an output volume or both).
outputwarp: (an existing file name)
Vector field that applies an equivalent warp as the BSpline. Maps positions from the
fixed coordinate frame to the moving coordinate frame. Optional.
resampledmovingfilename: (an existing file name)
Resampled moving image to fixed image coordinate frame. Optional (specify an output
transform or an output volume or both).
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L222
Wraps command ** Cast **
title: Cast Image
category: Filtering.Arithmetic
description: Cast a volume to a given data type. Use at your own risk when casting an input volume into a lower precision type! Allows casting to the same type as the input volume.
version: 0.1.0.$Revision: 2104 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/Cast
contributor: Nicole Aucoin, BWH (Ron Kikinis, BWH)
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
InputVolume: (an existing file name)
Input volume, the volume to cast.
OutputVolume: (a boolean or a file name)
Output volume, cast to the new type.
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
type: ('Char' or 'UnsignedChar' or 'Short' or 'UnsignedShort' or 'Int' or 'UnsignedInt'
or 'Float' or 'Double')
Type for the new output volume.
Outputs:
OutputVolume: (an existing file name)
Output volume, cast to the new type.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L285
Wraps command ** CheckerBoard **
description: Create a checkerboard volume of two volumes. The output volume will show the two inputs alternating according to the user supplied checkerPattern. This filter is often used to compare the results of image registration. Note that the second input is resampled to the same origin, spacing and direction before it is composed with the first input. The scalar type of the output volume will be the same as the input image scalar type.
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/CheckerBoard
contributor: Bill Lorensen
acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
checkerPattern: (an integer)
The pattern of input 1 and input 2 in the output image. The user can specify the number
of checkers in each dimension. A checkerPattern of 2,2,1 means that images will
alternate in every other checker in the first two dimensions. The same pattern will be
used in the 3rd dimension.
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume1: (an existing file name)
First Input volume
inputVolume2: (an existing file name)
Second Input volume
outputVolume: (a boolean or a file name)
Output filtered
Outputs:
outputVolume: (an existing file name)
Output filtered
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L357
Wraps command ** ComputeSUVBodyWeight **
title: SUVComputation
category: Quantification
description: Computes the standardized uptake value based on body weight. Takes an input PET image in DICOM and NRRD format (DICOM header must contain Radiopharmaceutical parameters). Produces a CSV file that contains patientID, studyDate, dose, labelID, suvmin, suvmax, suvmean, labelName for each volume of interest. It also displays some of the information as output strings in the GUI, the CSV file is optional in that case. The CSV file is appended to on each execution of the CLI.
version: 0.1.0.$Revision: 8595 $(alpha)
documentation-url: http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/ComputeSUVBodyWeight
contributor: Wendy Plesniak, BWH (Nicole Aucoin, BWH, Ron Kikinis, BWH)
acknowledgements: This work is funded by the Harvard Catalyst, and the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
OutputLabel: (a string)
List of labels for which SUV values were computed
OutputLabelValue: (a string)
List of label values for which SUV values were computed
SUVMax: (a string)
SUV max for each label
SUVMean: (a string)
SUV mean for each label
SUVMin: (a string)
SUV minimum for each label
args: (a string)
Additional parameters to the command
color: (an existing file name)
Color table to to map labels to colors and names
csvFile: (a boolean or a file name)
A file holding the output SUV values in comma separated lines, one per label. Optional.
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
labelMap: (an existing file name)
Input label volume containing the volumes of interest
petDICOMPath: (an existing directory name)
Input path to a directory containing a PET volume containing DICOM header information
for SUV computation
petVolume: (an existing file name)
Input PET volume for SUVbw computation (must be the same volume as pointed to by the
DICOM path!).
Outputs:
csvFile: (an existing file name)
A file holding the output SUV values in comma separated lines, one per label. Optional.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L423
Wraps command ** ConfidenceConnected **
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://www.slicer.org/slicerWiki/index.php/Modules:Simple_Region_Growing-Documentation-3.6
contributor: Jim Miller
acknowledgements: This command module was derived from Insight/Examples (copyright) Insight Software Consortium
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 volume to be filtered
iterations: (an integer)
Number of iterations of region growing
labelvalue: (an integer)
The integer value (0-255) to use for the segmentation results. This will determine the
color of the segmentation that will be generated by the Region growing algorithm
multiplier: (a float)
Number of standard deviations to include in intensity model
neighborhood: (an integer)
The radius of the neighborhood over which to calculate intensity model
outputVolume: (a boolean or a file name)
Output filtered
seed: (a list of from 3 to 3 items which are a float)
Seed point(s) for region growing
smoothingIterations: (an integer)
Number of smoothing iterations
timestep: (a float)
Timestep for curvature flow
Outputs:
outputVolume: (an existing file name)
Output filtered
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L488
Wraps command ** CurvatureAnisotropicDiffusion **
title: Curvature Anisotropic Diffusion
category: Filtering.Denoising
description: Performs anisotropic diffusion on an image using a modified curvature diffusion equation (MCDE).
MCDE does not exhibit the edge enhancing properties of classic anisotropic diffusion, which can under certain conditions undergo a ‘negative’ diffusion, which enhances the contrast of edges. Equations of the form of MCDE always undergo positive diffusion, with the conductance term only varying the strength of that diffusion.
Qualitatively, MCDE compares well with other non-linear diffusion techniques. It is less sensitive to contrast than classic Perona-Malik style diffusion, and preserves finer detailed structures in images. There is a potential speed trade-off for using this function in place of Gradient Anisotropic Diffusion. Each iteration of the solution takes roughly twice as long. Fewer iterations, however, may be required to reach an acceptable solution.
version: 0.1.0.$Revision: 18864 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/CurvatureAnisotropicDiffusion
contributor: Bill Lorensen
acknowledgements: This command module was derived from Insight/Examples (copyright) Insight Software Consortium
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
conductance: (a float)
Conductance controls the sensitivity of the conductance term. As a general rule, the
lower the value, the more strongly the filter preserves edges. A high value will cause
diffusion (smoothing) across edges. Note that the number of iterations controls how much
smoothing is done within regions bounded by edges.
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 volume to be filtered
iterations: (an integer)
The more iterations, the more smoothing. Each iteration takes the same amount of time.
If it takes 10 seconds for one iteration, then it will take 100 seconds for 10
iterations. Note that the conductance controls how much each iteration smooths across
edges.
outputVolume: (a boolean or a file name)
Output filtered
timeStep: (a float)
The time step depends on the dimensionality of the image. In Slicer the images are 3D
and the default (.0625) time step will provide a stable solution.
Outputs:
outputVolume: (an existing file name)
Output filtered
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L555
Wraps command ** DicomToNrrdConverter **
description: Converts diffusion weighted MR images in dicom series into Nrrd format for analysis in Slicer. This program has been tested on only a limited subset of DTI dicom formats available from Siemens, GE, and Phillips scanners. Work in progress to support dicom multi-frame data. The program parses dicom header to extract necessary information about measurement frame, diffusion weighting directions, b-values, etc, and write out a nrrd image. For non-diffusion weighted dicom images, it loads in an entire dicom series and writes out a single dicom volume in a .nhdr/.raw pair.
version: 0.2.0.$Revision: 916 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DicomToNrrdConverter
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: Xiaodong Tao
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. Additional support for DTI data produced on Philips scanners was contributed by Vincent Magnotta and Hans Johnson at the University of Iowa.
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
inputDicomDirectory: (an existing directory name)
Directory holding Dicom series
outputDirectory: (a boolean or a directory name)
Directory holding the output NRRD format
outputVolume: (a string)
Output filename (.nhdr or .nrrd)
smallGradientThreshold: (a float)
If a gradient magnitude is greater than 0 and less than smallGradientThreshold, then
DicomToNrrdConverter will display an error message and quit, unless the
useBMatrixGradientDirections option is set.
useBMatrixGradientDirections: (a boolean)
Fill the nhdr header with the gradient directions and bvalues computed out of the
BMatrix. Only changes behavior for Siemens data.
useIdentityMeaseurementFrame: (a boolean)
Adjust all the gradients so that the measurement frame is an identity matrix.
writeProtocolGradientsFile: (a boolean)
Write the protocol gradients to a file suffixed by ".txt" as they were specified in the
procol by multiplying each diffusion gradient direction by the measurement frame. This
file is for debugging purposes only, the format is not fixed, and will likely change as
debugging of new dicom formats is necessary.
Outputs:
outputDirectory: (an existing directory name)
Directory holding the output NRRD format
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L907
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 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DiffusionTensorEstimation
license: slicer3
contributor: Raul San Jose
acknowledgements: This command module is based on the estimation functionality provided by the Teem library. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('LS' or 'WLS')
LS: Least Squares, WLS: Weighted Least Squares
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DWI volume
mask: (an existing file name)
Mask where the tensors will be computed
outputBaseline: (a boolean or a file name)
Estimated baseline volume
outputTensor: (a boolean or a file name)
Estimated DTI volume
shiftNeg: (a boolean)
Shift eigenvalues so all are positive (accounts for bad tensors related to noise or
acquisition error)
Outputs:
outputBaseline: (an existing file name)
Estimated baseline volume
outputTensor: (an existing file name)
Estimated DTI volume
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L974
Wraps command ** DiffusionTensorMathematics **
version: 0.1.0.$Revision: 1892 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DiffusionTensorMathematics
contributor: Raul San Jose
acknowledgements: LMI
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
enumeration: ('Trace' or 'Determinant' or 'RelativeAnisotropy' or 'FractionalAnisotropy'
or 'Mode' or 'LinearMeasure' or 'PlanarMeasure' or 'SphericalMeasure' or
'MinEigenvalue' or 'MidEigenvalue' or 'MaxEigenvalue' or 'MaxEigenvalueProjectionX' or
'MaxEigenvalueProjectionY' or 'MaxEigenvalueProjectionZ' or 'RAIMaxEigenvecX' or
'RAIMaxEigenvecY' or 'RAIMaxEigenvecZ' or 'D11' or 'D22' or 'D33' or
'ParallelDiffusivity' or 'PerpendicularDffusivity')
An enumeration of strings
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input DTI volume
outputScalar: (a boolean or a file name)
Scalar volume derived from tensor
Outputs:
outputScalar: (an existing file name)
Scalar volume derived from tensor
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1036
Wraps command ** DiffusionTensorTest **
version: 0.1.0.$Revision: 18864 $(alpha)
contributor: Bill Lorensen
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputVolume: (an existing file name)
Input tensor volume to be filtered
outputVolume: (a boolean or a file name)
Filtered tensor volume
Outputs:
outputVolume: (an existing file name)
Filtered tensor volume
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1098
Wraps command ** DiffusionWeightedMasking **
description: <p>Performs a mask calculation from a diffusion weighted (DW) image.</p><p>Starting from a dw image, this module computes the baseline image averaging all the images without diffusion weighting and then applies the otsu segmentation algorithm in order to produce a mask. this mask can then be used when estimating the diffusion tensor (dt) image, not to estimate tensors all over the volume.</p>
version: 0.1.0.$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-0.5.3/nipype/interfaces/slicer/cli_modules.py#L645
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
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1559
Wraps command ** VBRAINSDemonWarp **
title: Vector Demon Registration (BRAINS)
category: Registration
version: 3.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:BRAINSDemonWarp
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
contributor: This tool was developed by Hans J. Johnson and Greg Harris.
acknowledgements: The development of this tool was supported by funding from grants NS050568 and NS40068 from the National Institute of Neurological Disorders and Stroke and grants MH31593, MH40856, from the National Institute of Mental Health.
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
arrayOfPyramidLevelIterations: (an integer)
The number of iterations for each pyramid level
backgroundFillValue: (an integer)
Replacement value to overwrite background when performing BOBF
checkerboardPatternSubdivisions: (an integer)
Number of Checkerboard subdivisions in all 3 directions
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
fixedBinaryVolume: (an existing file name)
Mask filename for desired region of interest in the Fixed image.
fixedVolume: (an existing file name)
Required: input fixed (target) image
gradient_type: ('0' or '1' or '2')
Type of gradient used for computing the demons force (0 is symmetrized, 1 is fixed
image, 2 is moving image)
gui: (a boolean)
Display intermediate image volumes for debugging
histogramMatch: (a boolean)
Histogram Match the input images. This is suitable for images of the same modality that
may have different absolute scales, but the same overall intensity profile.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
initializeWithDeformationField: (an existing file name)
Initial deformation field vector image file name
initializeWithTransform: (an existing file name)
Initial Transform filename
inputPixelType: ('float' or 'short' or 'ushort' or 'int' or 'uchar')
Input volumes will be typecast to this format: float|short|ushort|int|uchar
interpolationMode: ('NearestNeighbor' or 'Linear' or 'ResampleInPlace' or 'BSpline' or
'WindowedSinc' or 'Hamming' or 'Cosine' or 'Welch' or 'Lanczos' or 'Blackman')
Type of interpolation to be used when applying transform to moving volume. Options are
Linear, ResampleInPlace, NearestNeighbor, BSpline, or WindowedSinc
lowerThresholdForBOBF: (an integer)
Lower threshold for performing BOBF
makeBOBF: (a boolean)
Flag to make Brain-Only Background-Filled versions of the input and target volumes.
max_step_length: (a float)
Maximum length of an update vector (0: no restriction)
medianFilterSize: (an integer)
Median filter radius in all 3 directions. When images have a lot of salt and pepper
noise, this step can improve the registration.
minimumFixedPyramid: (an integer)
The shrink factor for the first level of the fixed image pyramid. (i.e. start at 1/16
scale, then 1/8, then 1/4, then 1/2, and finally full scale)
minimumMovingPyramid: (an integer)
The shrink factor for the first level of the moving image pyramid. (i.e. start at 1/16
scale, then 1/8, then 1/4, then 1/2, and finally full scale)
movingBinaryVolume: (an existing file name)
Mask filename for desired region of interest in the Moving image.
movingVolume: (an existing file name)
Required: input moving image
neighborhoodForBOBF: (an integer)
neighborhood in all 3 directions to be included when performing BOBF
numberOfBCHApproximationTerms: (an integer)
Number of terms in the BCH expansion
numberOfHistogramBins: (an integer)
The number of histogram levels
numberOfMatchPoints: (an integer)
The number of match points for histrogramMatch
numberOfPyramidLevels: (an integer)
Number of image pyramid levels to use in the multi-resolution registration.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputCheckerboardVolume: (a boolean or a file name)
Genete a checkerboard image volume between the fixedVolume and the deformed
movingVolume.
outputDebug: (a boolean)
Flag to write debugging images after each step.
outputDeformationFieldVolume: (a boolean or a file name)
Output deformation field vector image (will have the same physical space as the
fixedVolume).
outputDisplacementFieldPrefix: (a string)
Displacement field filename prefix for writing separate x, y, and z component images
outputNormalized: (a boolean)
Flag to warp and write the normalized images to output. In normalized images the image
values are fit-scaled to be between 0 and the maximum storage type value.
outputPixelType: ('float' or 'short' or 'ushort' or 'int' or 'uchar')
outputVolume will be typecast to this format: float|short|ushort|int|uchar
outputVolume: (a boolean or a file name)
Required: output resampled moving image (will have the same physical space as the
fixedVolume).
promptUser: (a boolean)
Prompt the user to hit enter each time an image is sent to the DebugImageViewer
registrationFilterType: ('Demons' or 'FastSymmetricForces' or 'Diffeomorphic' or
'LogDemons' or 'SymmetricLogDemons')
Registration Filter Type:
Demons|FastSymmetricForces|Diffeomorphic|LogDemons|SymmetricLogDemons
seedForBOBF: (an integer)
coordinates in all 3 directions for Seed when performing BOBF
smoothDeformationFieldSigma: (a float)
A gaussian smoothing value to be applied to the deformation feild at each iteration.
upFieldSmoothing: (a float)
Smoothing sigma for the update field at each iteration
upperThresholdForBOBF: (an integer)
Upper threshold for performing BOBF
use_vanilla_dem: (a boolean)
Run vanilla demons algorithm
weightFactors: (a float)
Weight fatctors for each input images
Outputs:
outputCheckerboardVolume: (an existing file name)
Genete a checkerboard image volume between the fixedVolume and the deformed
movingVolume.
outputDeformationFieldVolume: (an existing file name)
Output deformation field vector image (will have the same physical space as the
fixedVolume).
outputVolume: (an existing file name)
Required: output resampled moving image (will have the same physical space as the
fixedVolume).
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L714
Wraps command ** dwiNoiseFilter **
title: Rician LMMSE Image Filter
category: Diffusion.Denoising
description: This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower). Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead. A complete description of the algorithm in this module can be found in: S. Aja-Fernandez, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. Restoration of DWI data using a Rician LMMSE estimator. IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.
version: 0.1.1.$Revision: 1 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/RicianLMMSEImageFilter
contributor: Antonio Tristan Vega, Santiago Aja Fernandez and Marc Niethammer. Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
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
hrf: (a float)
How many histogram bins per unit interval.
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.
iter: (an integer)
Number of iterations for the noise removal filter.
maxnstd: (an integer)
Maximum allowed noise standard deviation.
minnstd: (an integer)
Minimum allowed noise standard deviation.
mnve: (an integer)
Minimum number of voxels in kernel used for estimation.
mnvf: (an integer)
Minimum number of voxels in kernel used for filtering.
outputVolume: (a boolean or a file name)
Output DWI volume.
re: (an integer)
Estimation radius.
rf: (an integer)
Filtering radius.
uav: (a boolean)
Use absolute value in case of negative square.
Outputs:
outputVolume: (an existing file name)
Output DWI volume.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L778
Wraps command ** dwiUNLM **
title: Unbiased Non Local Means filter for DWI
category: Legacy.Diffusion.Denoising
description: This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the images using a Unbiased Non Local Means for Rician noise algorithm. It exploits not only the spatial redundancy, but the redundancy in similar gradient directions as well; it takes into account the N closest gradient directions to the direction being processed (a maximum of 5 gradient directions is allowed to keep a reasonable computational load, since we do not use neither similarity maps nor block-wise implementation). The noise parameter is automatically estimated in the same way as in the jointLMMSE module. A complete description of the algorithm may be found in: Antonio Tristan-Vega and Santiago Aja-Fernandez, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010. Please, note that the execution of this filter is extremely slow, son only very conservative parameters (block size and search size as small as possible) should be used. Even so, its execution may take several hours. The advantage of this filter over joint LMMSE is its better preservation of edges and fine structures.
version: 0.0.1.$Revision: 1 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/UnbiasedNonLocalMeansFilterForDWI
contributor: Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
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
hp: (a float)
This parameter is related to noise; the larger the parameter, the more agressive the
filtering. Should be near 1, and only values between 0.8 and 1.2 are allowed
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.
ng: (an integer)
The number of the closest gradients that are used to jointly filter a given gradient
direction (a maximum of 5 is allowed).
outputVolume: (a boolean or a file name)
Output DWI volume.
rc: (an integer)
Similarity between blocks is measured using windows of this size.
re: (an integer)
A neighborhood of this size is used to compute the statistics for noise estimation.
rs: (an integer)
The algorithm search for similar voxels in a neighborhood of this size (larger sizes
than the default one are extremely slow).
Outputs:
outputVolume: (an existing file name)
Output DWI volume.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1623
Wraps command ** extractNrrdVectorIndex **
title: Extract Nrrd Index
category: Diffusion.GTRACT
description: This program will extract a 3D image (single vector) from a vector 3D image at a given vector index.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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)
Required: input file containing the vector that will be extracted
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the vector image at the given index
setImageOrientation: ('AsAcquired' or 'Axial' or 'Coronal' or 'Sagittal')
Sets the image orientation of the extracted vector (Axial, Coronal, Sagittal)
vectorIndex: (an integer)
Index in the vector image to extract
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the vector image at the given index
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1683
Wraps command ** gtractAnisotropyMap **
title: Anisotropy Map
category: Diffusion.GTRACT
description: This program will generate a scalar map of anisotropy, given a tensor representation. Anisotropy images are used for fiber tracking, but the anisotropy scalars are not defined along the path. Instead, the tensor representation is included as point data allowing all of these metrics to be computed using only the fiber tract point data. The images can be saved in any ITK supported format, but it is suggested that you use an image format that supports the definition of the image origin. This includes NRRD, NifTI, and Meta formats. These images can also be used for scalar analysis including regional anisotropy measures or VBM style analysis.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
anisotropyType: ('ADC' or 'FA' or 'RA' or 'VR' or 'AD' or 'RD' or 'LI')
Anisotropy Mapping Type: ADC, FA, RA, VR, AD, RD, LI
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
inputTensorVolume: (an existing file name)
Required: input file containing the diffusion tensor image
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the selected kind of anisotropy scalar.
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the selected kind of anisotropy scalar.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1744
Wraps command ** gtractAverageBvalues **
title: Average B-Values
category: Diffusion.GTRACT
description: This program will directly average together the baseline gradients (b value equals 0) within a DWI scan. This is usually used after gtractCoregBvalues.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
averageB0only: (a boolean)
Average only baseline gradients. All other gradient directions are not averaged, but
retained in the outputVolume
directionsTolerance: (a float)
Tolerance for matching identical gradient direction pairs
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)
Required: input image file name containing multiple baseline gradients to average
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing directly averaged baseline images
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing directly averaged baseline images
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1805
Wraps command ** gtractClipAnisotropy **
title: Clip Anisotropy
category: Diffusion.GTRACT
description: This program will zero the first and/or last slice of an anisotropy image, creating a clipped anisotropy image.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
clipFirstSlice: (a boolean)
Clip the first slice of the anisotropy image
clipLastSlice: (a boolean)
Clip the last slice of the anisotropy image
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)
Required: input image file name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the clipped anisotropy image
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the clipped anisotropy image
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1884
Wraps command ** gtractCoRegAnatomy **
title: Coregister B0 to Anatomy B-Spline
category: Diffusion.GTRACT
description: This program will register a Nrrd diffusion weighted 4D vector image to a fixed anatomical image. Two registration methods are supported for alignment with anatomical images: Rigid and B-Spline. The rigid registration performs a rigid body registration with the anatomical images and should be done as well to initialize the B-Spline transform. The B-SPline transform is the deformable transform, where the user can control the amount of deformation based on the number of control points as well as the maximum distance that these points can move. The B-Spline registration places a low dimensional grid in the image, which is deformed. This allows for some susceptibility related distortions to be removed from the diffusion weighted images. In general the amount of motion in the slice selection and read-out directions direction should be kept low. The distortion is in the phase encoding direction in the images. It is recommended that skull stripped (i.e. image containing only brain with skull removed) images shoud be used for image co-registration with the B-Spline transform.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
borderSize: (an integer)
Size of border
convergence: (a float)
Convergence Factor
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
gradientTolerance: (a float)
Gradient Tolerance
gridSize: (an integer)
Number of grid subdivisions in all 3 directions
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
inputAnatomicalVolume: (an existing file name)
Required: input anatomical image file name. It is recommended that that the input
anatomical image has been skull stripped and has the same orientation as the DWI scan.
inputRigidTransform: (an existing file name)
Required (for B-Spline type co-registration): input rigid transform file name. Used as a
starting point for the anatomical B-Spline registration.
inputVolume: (an existing file name)
Required: input vector image file name. It is recommended that the input volume is the
skull stripped baseline image of the DWI scan.
maxBSplineDisplacement: (a float)
Sets the maximum allowed displacements in image physical coordinates for BSpline
control grid along each axis. A value of 0.0 indicates that the problem should be
unbounded. NOTE: This only constrains the BSpline portion, and does not limit the
displacement from the associated bulk transform. This can lead to a substantial
reduction in computation time in the BSpline optimizer.,
maximumStepSize: (a float)
Maximum permitted step size to move in the selected 3D fit
minimumStepSize: (a float)
Minimum required step size to move in the selected 3D fit without converging -- decrease
this to make the fit more exacting
numberOfHistogramBins: (an integer)
Number of histogram bins
numberOfIterations: (an integer)
Number of iterations in the selected 3D fit
numberOfSamples: (an integer)
Number of voxels sampled for mutual information computation in the selected 3D fit
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputTransformName: (a boolean or a file name)
Required: filename for the fit transform.
relaxationFactor: (a float)
Fraction of gradient from Jacobian to attempt to move in the selected 3D fit
spatialScale: (an integer)
Scales the number of voxels in the image by this value to specify the number of voxels
used in the registration
transformType: ('Rigid' or 'Bspline')
Transform Type: Rigid|Bspline
translationScale: (a float)
How much to scale up changes in position compared to unit rotational changes in radians
-- decrease this to put more translation in the fit
useCenterOfHeadAlign: (a boolean)
CenterOfHeadAlign attempts to find a hemisphere full of foreground voxels from the
superior direction as an estimate of where the center of a head shape would be to drive
a center of mass estimate. Perform a CenterOfHeadAlign registration as part of the
sequential registration steps. This option MUST come first, and CAN NOT be used with
either MomentsAlign, GeometryAlign, or initialTransform file. This family of options
superceeds the use of transformType if any of them are set.
useGeometryAlign: (a boolean)
GeometryAlign on assumes that the center of the voxel lattice of the images represent
similar structures. Perform a GeometryCenterAlign registration as part of the sequential
registration steps. This option MUST come first, and CAN NOT be used with either
MomentsAlign, CenterOfHeadAlign, or initialTransform file. This family of options
superceeds the use of transformType if any of them are set.
useMomentsAlign: (a boolean)
MomentsAlign assumes that the center of mass of the images represent similar structures.
Perform a MomentsAlign registration as part of the sequential registration steps. This
option MUST come first, and CAN NOT be used with either CenterOfHeadLAlign,
GeometryAlign, or initialTransform file. This family of options superceeds the use of
transformType if any of them are set.
vectorIndex: (an integer)
Vector image index in the moving image (within the DWI) to be used for registration.
Outputs:
outputTransformName: (an existing file name)
Required: filename for the fit transform.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L1943
Wraps command ** gtractConcatDwi **
title: Concat DWI Images
category: Diffusion.GTRACT
description: This program will concatenate two DTI runs together.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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)
Required: input file containing the first diffusion weighted image
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the combined diffusion weighted images.
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the combined diffusion weighted images.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2003
Wraps command ** gtractCopyImageOrientation **
title: Copy Image Orientation
category: Diffusion.GTRACT
description: This program will copy the orientation from the reference image into the moving image. Currently, the registration process requires that the diffusion weighted images and the anatomical images have the same image orientation (i.e. Axial, Coronal, Sagittal). It is suggested that you copy the image orientation from the diffusion weighted images and apply this to the anatomical image. This image can be subsequently removed after the registration step is complete. We anticipate that this limitation will be removed in future versions of the registration programs.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputReferenceVolume: (an existing file name)
Required: input file containing orietation that will be cloned.
inputVolume: (an existing file name)
Required: input file containing the signed short image to reorient without resampling.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD or Nifti file containing the reoriented image in reference
image space.
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD or Nifti file containing the reoriented image in reference
image space.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2075
Wraps command ** gtractCoregBvalues **
title: Coregister B-Values
category: Diffusion.GTRACT
description: This step should be performed after converting DWI scans from DICOM to NRRD format. This program will register all gradients in a NRRD diffusion weighted 4D vector image (moving image) to a specified index in a fixed image. It also supports co-registration with a T2 weighted image or field map in the same plane as the DWI data. The fixed image for the registration should be a b0 image. A mutual information metric cost function is used for the registration because of the differences in signal intensity as a result of the diffusion gradients. The full affine allows the registration procedure to correct for eddy current distortions that may exist in the data. If the eddyCurrentCorrection is enabled, relaxationFactor (0.25) and maximumStepSize (0.1) should be adjusted.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
debugLevel: (an integer)
Display debug messages, and produce debug intermediate results. 0=OFF, 1=Minimal,
10=Maximum debugging.
eddyCurrentCorrection: (a boolean)
Flag to perform eddy current corection in addition to motion correction (recommended)
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
fixedVolume: (an existing file name)
Required: input fixed image file name. It is recommended that this image should either
contain or be a b0 image.
fixedVolumeIndex: (an integer)
Index in the fixed image for registration. It is recommended that this image should be a
b0 image.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
maximumStepSize: (a float)
Maximum permitted step size to move in each 3D fit step (adjust when
eddyCurrentCorrection is enabled; suggested value = 0.1)
minimumStepSize: (a float)
Minimum required step size to move in each 3D fit step without converging -- decrease
this to make the fit more exacting
movingVolume: (an existing file name)
Required: input moving image file name. In order to register gradients within a scan to
its first gradient, set the movingVolume and fixedVolume as the same image.
numberOfIterations: (an integer)
Number of iterations in each 3D fit
numberOfSpatialSamples: (an integer)
Number of voxels sampled for mutual information computation in each 3D fit step
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputTransform: (a boolean or a file name)
Registration 3D transforms concatenated in a single output file. There are no tools
that can use this, but can be used for debugging purposes.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing moving images individually resampled and
fit to the specified fixed image index.
registerB0Only: (a boolean)
Register the B0 images only
relaxationFactor: (a float)
Fraction of gradient from Jacobian to attempt to move in each 3D fit step (adjust when
eddyCurrentCorrection is enabled; suggested value = 0.25)
spatialScale: (a float)
How much to scale up changes in position compared to unit rotational changes in radians
-- decrease this to put more rotation in the fit
Outputs:
outputTransform: (an existing file name)
Registration 3D transforms concatenated in a single output file. There are no tools
that can use this, but can be used for debugging purposes.
outputVolume: (an existing file name)
Required: name of output NRRD file containing moving images individually resampled and
fit to the specified fixed image index.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2142
Wraps command ** gtractCostFastMarching **
title: Cost Fast Marching
category: Diffusion.GTRACT
description: This program will use a fast marching fiber tracking algorithm to identify fiber tracts from a tensor image. This program is the first portion of the algorithm. The user must first run gtractFastMarchingTracking to generate the actual fiber tracts. This algorithm is roughly based on the work by G. Parker et al. from IEEE Transactions On Medical Imaging, 21(5): 505-512, 2002. An additional feature of including anisotropy into the vcl_cost function calculation is included.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris. The original code here was developed by Daisy Espino.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
anisotropyWeight: (a float)
Anisotropy weight used for vcl_cost function calculations
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
inputAnisotropyVolume: (an existing file name)
Required: input anisotropy image file name
inputStartingSeedsLabelMapVolume: (an existing file name)
Required: input starting seeds LabelMap image file name
inputTensorVolume: (an existing file name)
Required: input tensor image file name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputCostVolume: (a boolean or a file name)
Output vcl_cost image
outputSpeedVolume: (a boolean or a file name)
Output speed image
seedThreshold: (a float)
Anisotropy threshold used for seed selection
startingSeedsLabel: (an integer)
Label value for Starting Seeds
stoppingValue: (a float)
Terminiating value for vcl_cost function estimation
Outputs:
outputCostVolume: (an existing file name)
Output vcl_cost image
outputSpeedVolume: (an existing file name)
Output speed image
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2202
Wraps command ** gtractImageConformity **
title: Image Conformity
category: Diffusion.GTRACT
description: This program will straighten out the Direction and Origin to match the Reference Image.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputReferenceVolume: (an existing file name)
Required: input file containing the standard image to clone the characteristics of.
inputVolume: (an existing file name)
Required: input file containing the signed short image to reorient without resampling.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output Nrrd or Nifti file containing the reoriented image in reference
image space.
Outputs:
outputVolume: (an existing file name)
Required: name of output Nrrd or Nifti file containing the reoriented image in reference
image space.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2263
Wraps command ** gtractInvertBSplineTransform **
title: B-Spline Transform Inversion
category: Diffusion.GTRACT
description: This program will invert a B-Spline transform using a thin-plate spline approximation.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputReferenceVolume: (an existing file name)
Required: input image file name to exemplify the anatomical space to interpolate over.
inputTransform: (an existing file name)
Required: input B-Spline transform file name
landmarkDensity: (an integer)
Number of landmark subdivisions in all 3 directions
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputTransform: (a boolean or a file name)
Required: output transform file name
Outputs:
outputTransform: (an existing file name)
Required: output transform file name
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2324
Wraps command ** gtractInvertDeformationField **
title: Invert Deformation Field
category: Diffusion.GTRACT
description: This program will invert a deformatrion field. The size of the deformation field is defined by an example image provided by the user
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
baseImage: (an existing file name)
Required: base image used to define the size of the inverse field
deformationImage: (an existing file name)
Required: Deformation field image
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
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: Output deformation field
subsamplingFactor: (an integer)
Subsampling factor for the deformation field
Outputs:
outputVolume: (an existing file name)
Required: Output deformation field
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2383
Wraps command ** gtractInvertRigidTransform **
title: Rigid Transform Inversion
category: Diffusion.GTRACT
description: This program will invert a Rigid transform.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputTransform: (an existing file name)
Required: input rigid transform file name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputTransform: (a boolean or a file name)
Required: output transform file name
Outputs:
outputTransform: (an existing file name)
Required: output transform file name
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2445
Wraps command ** gtractResampleAnisotropy **
title: Resample Anisotropy
category: Diffusion.GTRACT
description: This program will resample a floating point image using either the Rigid or B-Spline transform. You may want to save the aligned B0 image after each of the anisotropy map co-registration steps with the anatomical image to check the registration quality with another tool.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputAnatomicalVolume: (an existing file name)
Required: input file containing the anatomical image whose characteristics will be
cloned.
inputAnisotropyVolume: (an existing file name)
Required: input file containing the anisotropy image
inputTransform: (an existing file name)
Required: input Rigid OR Bspline transform file name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the resampled transformed anisotropy
image.
transformType: ('Rigid' or 'B-Spline')
Transform type: Rigid, B-Spline
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the resampled transformed anisotropy
image.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2508
Wraps command ** gtractResampleB0 **
title: Resample B0
category: Diffusion.GTRACT
description: This program will resample a signed short image using either a Rigid or B-Spline transform. The user must specify a template image that will be used to define the origin, orientation, spacing, and size of the resampled image.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputAnatomicalVolume: (an existing file name)
Required: input file containing the anatomical image defining the origin, spacing and
size of the resampled image (template)
inputTransform: (an existing file name)
Required: input Rigid OR Bspline transform file name
inputVolume: (an existing file name)
Required: input file containing the 4D image
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the resampled input image.
transformType: ('Rigid' or 'B-Spline')
Transform type: Rigid, B-Spline
vectorIndex: (an integer)
Index in the diffusion weighted image set for the B0 image
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the resampled input image.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2570
Wraps command ** gtractResampleCodeImage **
title: Resample Code Image
category: Diffusion.GTRACT
description: This program will resample a short integer code image using either the Rigid or Inverse-B-Spline transform. The reference image is the DTI tensor anisotropy image space, and the input code image is in anatomical space.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputCodeVolume: (an existing file name)
Required: input file containing the code image
inputReferenceVolume: (an existing file name)
Required: input file containing the standard image to clone the characteristics of.
inputTransform: (an existing file name)
Required: input Rigid or Inverse-B-Spline transform file name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the resampled code image in acquisition
space.
transformType: ('Rigid' or 'Affine' or 'B-Spline' or 'Inverse-B-Spline' or 'None')
Transform type: Rigid or Inverse-B-Spline
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the resampled code image in acquisition
space.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2631
Wraps command ** gtractResampleDWIInPlace **
title: Resample DWI In Place
category: Diffusion.GTRACT
description: Resamples DWI image to structural image.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
debugLevel: (an integer)
Display debug messages, and produce debug intermediate results. 0=OFF, 1=Minimal,
10=Maximum debugging.
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
inputTransform: (an existing file name)
Required: transform file derived from rigid registration of b0 image to reference
structural image.
inputVolume: (an existing file name)
Required: input image is a 4D NRRD image.
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: output image (NRRD file) that has been transformed into the space of the
structural image.
Outputs:
outputVolume: (an existing file name)
Required: output image (NRRD file) that has been transformed into the space of the
structural image.
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2699
Wraps command ** gtractTensor **
title: Tensor Estimation
category: Diffusion.GTRACT
description: This step will convert a b-value averaged diffusion tensor image to a 3x3 tensor voxel image. This step takes the diffusion tensor image data and generates a tensor representation of the data based on the signal intensity decay, b values applied, and the diffusion difrections. The apparent diffusion coefficient for a given orientation is computed on a pixel-by-pixel basis by fitting the image data (voxel intensities) to the Stejskal-Tanner equation. If at least 6 diffusion directions are used, then the diffusion tensor can be computed. This program uses itk::DiffusionTensor3DReconstructionImageFilter. The user can adjust background threshold, median filter, and isotropic resampling.
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta and Greg Harris.
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
Inputs:
[Mandatory]
[Optional]
applyMeasurementFrame: (a boolean)
Flag to apply the measurement frame to the gradient directions
args: (a string)
Additional parameters to the command
b0Index: (an integer)
Index in input vector index to extract
backgroundSuppressingThreshold: (an integer)
Image threshold to suppress background. This sets a threshold used on the b0 image to
remove background voxels from processing. Typically, values of 100 and 500 work well for
Siemens and GE DTI data, respectively. Check your data particularly in the globus
pallidus to make sure the brain tissue is not being eliminated with this threshold.
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
ignoreIndex: (an integer)
Ignore diffusion gradient index. Used to remove specific gradient directions with
artifacts.
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)
Required: input image 4D NRRD image. Must contain data based on at least 6 distinct
diffusion directions. The inputVolume is allowed to have multiple b0 and gradient
direction images. Averaging of the b0 image is done internally in this step. Prior
averaging of the DWIs is not required.
maskProcessingMode: ('NOMASK' or 'ROIAUTO' or 'ROI')
ROIAUTO: mask is implicitly defined using a otsu forground and hole filling algorithm.
ROI: Uses the masks to define what parts of the image should be used for computing the
transform. NOMASK: no mask used
maskVolume: (an existing file name)
Mask Image, if maskProcessingMode is ROI
medianFilterSize: (an integer)
Median filter radius in all 3 directions
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputVolume: (a boolean or a file name)
Required: name of output NRRD file containing the Tensor vector image
resampleIsotropic: (a boolean)
Flag to resample to isotropic voxels. Enabling this feature is recommended if fiber
tracking will be performed.
size: (a float)
Isotropic voxel size to resample to
Outputs:
outputVolume: (an existing file name)
Required: name of output NRRD file containing the Tensor vector image
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L2759
Wraps command ** gtractTransformToDeformationField **
title: Create Deformation Field
category: Diffusion.GTRACT
description: This program will compute forward deformation from the given Transform. The size of the DF is equal to MNI space
version: 4.0.0
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Modules:GTRACT
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
contributor: This tool was developed by Vincent Magnotta, Madhura Ingalhalikar, and Greg Harris
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
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
inputReferenceVolume: (an existing file name)
Required: input image file name to exemplify the anatomical space over which to
vcl_express the transform as a displacement field.
inputTransform: (an existing file name)
Input Transform File Name
numberOfThreads: (an integer)
Explicitly specify the maximum number of threads to use.
outputDeformationFieldVolume: (a boolean or a file name)
Output deformation field
Outputs:
outputDeformationFieldVolume: (an existing file name)
Output deformation field
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/slicer/cli_modules.py#L842
Wraps command ** jointLMMSE **
title: Joint Rician LMMSE Image Filter
category: Diffusion.Denoising
description: This module reduces Rician noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process. The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram. A complete description of the algorithm may be found in: Antonio Tristan-Vega and Santiago Aja-Fernandez, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.
version: 0.1.1.$Revision: 1 $(alpha)
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/JointRicianLMMSEImageFilter
contributor: Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
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.
ng: (an integer)
The number of the closest gradients that are used to jointly filter a given gradient
direction (0 to use all).
outputVolume: (a boolean or a file name)
Output DWI volume.
re: (an integer)
Estimation radius.
rf: (an integer)
Filtering radius.
Outputs:
outputVolume: (an existing file name)
Output DWI volume.