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interfaces.slicer.cli_modules

Add

Link to code

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

AffineRegistration

Link to code

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).

BRAINSDemonWarp

Link to code

Wraps command ** BRAINSDemonWarp **

title: Demon Registration (BRAINS)

category: Registration

description:
This program finds a deformation field to warp a moving image onto a fixed image. The images must be of the same signal kind, and contain an image of the same kind of object. This program uses the Thirion Demons warp software in ITK, the Insight Toolkit. Additional information is available at: http://www.nitrc.org/projects/brainsdemonwarp.

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).

BRAINSFit

Link to code

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.

BRAINSROIAuto

Link to code

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.

BRAINSResample

Link to code

Wraps command ** BRAINSResample **

title: Resample Image (BRAINS)

category: Registration

description:
This program collects together three common image processing tasks that all involve resampling an image volume: Resampling to a new resolution and spacing, applying a transformation (using an ITK transform IO mechanisms) and Warping (using a vector image deformation field). Full documentation available here: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/BRAINSResample.

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

BSplineDeformableRegistration

Link to code

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).

Cast

Link to code

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.

CheckerBoard

Link to code

Wraps command ** CheckerBoard **

title:
CheckerBoard Filter
category:
Filtering

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

ComputeSUVBodyWeight

Link to code

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.

ConfidenceConnected

Link to code

Wraps command ** ConfidenceConnected **

title:
Simple region growing
category:
Segmentation
description:
A simple region growing segmentation algorithm based on intensity statistics. To create a list of fiducials (Seeds) for this algorithm, click on the tool bar icon of an arrow pointing to a starburst fiducial to enter the ‘place a new object mode’ and then use the fiducials module. This module uses the Slicer Command Line Interface (CLI) and the ITK filters CurvatureFlowImageFilter and ConfidenceConnectedImageFilter.

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

CurvatureAnisotropicDiffusion

Link to code

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

DicomToNrrdConverter

Link to code

Wraps command ** DicomToNrrdConverter **

title:
Dicom to Nrrd Converter
category:
Converters

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

DiffusionTensorEstimation

Link to code

Wraps command ** DiffusionTensorEstimation **

title:
Diffusion Tensor Estimation
category:
Diffusion.Utilities
description:
Performs a tensor model estimation from diffusion weighted images.

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

DiffusionTensorMathematics

Link to code

Wraps command ** DiffusionTensorMathematics **

title:
Diffusion Tensor Scalar Measurements
category:
Diffusion.Utilities
description:
Compute a set of different scalar measurements from a tensor field, specially oriented for Diffusion Tensors where some rotationally invariant measurements, like Fractional Anisotropy, are highly used to describe the anistropic behaviour of the tensor.

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

DiffusionTensorTest

Link to code

Wraps command ** DiffusionTensorTest **

title:
Simple IO Test
category:
Legacy.Work in Progress.Diffusion Tensor.Test
description:
Simple test of tensor IO

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

DiffusionWeightedMasking

Link to code

Wraps command ** DiffusionWeightedMasking **

title:
Mask from Diffusion Weighted Images
category:
Diffusion.Utilities

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

ResampleDTI

Link to code

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

VBRAINSDemonWarp

Link to code

Wraps command ** VBRAINSDemonWarp **

title: Vector Demon Registration (BRAINS)

category: Registration

description:
This program finds a deformation field to warp a moving image onto a fixed image. The images must be of the same signal kind, and contain an image of the same kind of object. This program uses the Thirion Demons warp software in ITK, the Insight Toolkit. Additional information is available at: http://www.nitrc.org/projects/brainsdemonwarp.

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).

dwiNoiseFilter

Link to code

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.

dwiUNLM

Link to code

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.

extractNrrdVectorIndex

Link to code

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

gtractAnisotropyMap

Link to code

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.

gtractAverageBvalues

Link to code

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

gtractClipAnisotropy

Link to code

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

gtractCoRegAnatomy

Link to code

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.

gtractConcatDwi

Link to code

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.

gtractCopyImageOrientation

Link to code

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.

gtractCoregBvalues

Link to code

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.

gtractCostFastMarching

Link to code

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

gtractImageConformity

Link to code

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.

gtractInvertBSplineTransform

Link to code

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

gtractInvertDeformationField

Link to code

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

gtractInvertRigidTransform

Link to code

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

gtractResampleAnisotropy

Link to code

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.

gtractResampleB0

Link to code

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.

gtractResampleCodeImage

Link to code

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.

gtractResampleDWIInPlace

Link to code

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.

gtractTensor

Link to code

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

gtractTransformToDeformationField

Link to code

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

jointLMMSE

Link to code

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.