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# interfaces.brainsuite.brainsuite

## BDP

Link to code

Wraps command bdp.sh

BrainSuite Diffusion Pipeline (BDP) enables fusion of diffusion and structural MRI information for advanced image and connectivity analysis. It provides various methods for distortion correction, co-registration, diffusion modeling (DTI and ODF) and basic ROI-wise statistic. BDP is a flexible and diverse tool which supports wide variety of diffusion datasets. For more information, please see:

http://brainsuite.org/processing/diffusion/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> bdp = brainsuite.BDP()
    >>> bdp.inputs.bfcFile = '/directory/subdir/prefix.bfc.nii.gz'
    >>> bdp.inputs.inputDiffusionData = '/directory/subdir/prefix.dwi.nii.gz'
    >>> bdp.inputs.BVecBValPair = ['/directory/subdir/prefix.dwi.bvec', '/directory/subdir/prefix.dwi.bval']
    >>> results = bdp.run()

Inputs:

    [Mandatory]
    BVecBValPair: (a list of from 2 to 2 items which are a unicode
             string)
            Must input a list containing first the BVector file, then the BValue
            file (both must be absolute paths)
            Example: bdp.inputs.BVecBValPair =
            ['/directory/subdir/prefix.dwi.bvec',
            '/directory/subdir/prefix.dwi.bval'] The first item in the list
            specifies the filename of the file containing b-values for the
            diffusion scan. The b-value file must be a plain-text file and
            usually has an extension of .bval
            The second item in the list specifies the filename of the file
            containing the diffusion gradient directions (specified in the voxel
            coordinates of the input diffusion-weighted image)The b-vectors file
            must be a plain text file and usually has an extension of .bvec
            flag: --bvec %s --bval %s, position: -1
            mutually_exclusive: bMatrixFile
    bMatrixFile: (a file name)
            Specifies the absolute path and filename of the file containing
            b-matrices for diffusion-weighted scans. The flag must be followed
            by the filename. This file must be a plain text file containing 3x3
            matrices for each diffusion encoding direction. It should contain
            zero matrices corresponding to b=0 images. This file usually has
            ".bmat" as its extension, and can be used to provide BDP with the
            more-accurate b-matrices as saved by some proprietary scanners. The
            b-matrices specified by the file must be in the voxel coordinates of
            the input diffusion weighted image (NIfTI file). In case b-matrices
            are not known/calculated, bvec and .bval files can be used instead
            (see diffusionGradientFile and bValueFile).
            flag: --bmat %s, position: -1
            mutually_exclusive: BVecBValPair
    bfcFile: (a file name)
            Specify absolute path to file produced by bfc. By default, bfc
            produces the file in the format: prefix.bfc.nii.gz
            flag: %s, position: 0
            mutually_exclusive: noStructuralRegistration
    inputDiffusionData: (a file name)
            Specifies the absolute path and filename of the input diffusion data
            in 4D NIfTI-1 format. The flag must be followed by the filename.
            Only NIfTI-1 files with extension .nii or .nii.gz are supported.
            Furthermore, either bMatrixFile, or a combination of both bValueFile
            and diffusionGradientFile must be used to provide the necessary
            b-matrices/b-values and gradient vectors.
            flag: --nii %s, position: -2
    noStructuralRegistration: (a boolean)
            Allows BDP to work without any structural input. This can useful
            when one is only interested in diffusion modelling part of BDP. With
            this flag only fieldmap-based distortion correction is supported.
            outPrefix can be used to specify fileprefix of the output filenames.
            Change dwiMask to define region of interest for diffusion modelling.
            flag: --no-structural-registration, position: 0
            mutually_exclusive: bfcFile

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    bValRatioThreshold: (a float)
            Sets a threshold which is used to determine b=0 images. When there
            are no diffusion weighted image with b-value of zero, then BDP tries
            to use diffusion weighted images with a low b-value in place of b=0
            image. The diffusion images with minimum b-value is used as b=0
            image only if the ratio of the maximum and minimum b-value is more
            than the specified threshold. A lower value of threshold will allow
            diffusion images with higher b-value to be used as b=0 image. The
            default value of this threshold is set to 45, if this trait is not
            set.
            flag: --bval-ratio-threshold %f
    customDiffusionLabel: (a file name)
            BDP supports custom ROIs in addition to those generated by
            BrainSuite SVReg) for ROI-wise statistics calculation. The flag must
            be followed by the name of either a file (custom ROI file) or of a
            folder that contains one or more ROI files. All of the files must be
            in diffusion coordinate, i.e. the label files should overlay
            correctly with the diffusion scan in BrainSuite. These input label
            files are also transferred (and saved) to T1 coordinate for
            statistics in T1 coordinate. BDP uses nearest-neighborhood
            interpolation for this transformation. Only NIfTI files, with an
            extension of .nii or .nii.gz are supported. In order to avoid
            confusion with other ROI IDs in the statistic files, a 5-digit ROI
            ID is generated for each custom label found and the mapping of ID to
            label file is saved in the file fileprefix>.BDP_ROI_MAP.xml. Custom
            label files can also be generated by using the label painter tool in
            BrainSuite. See also customLabelXML
            flag: --custom-diffusion-label %s
    customLabelXML: (a file name)
            BrainSuite saves a descriptions of the SVReg labels (ROI name, ID,
            color, and description) in an .xml file
            brainsuite_labeldescription.xml). BDP uses the ROI ID"s from this
            xml file to report statistics. This flag allows for the use of a
            custom label description xml file. The flag must be followed by an
            xml filename. This can be useful when you want to limit the ROIs for
            which you compute statistics. You can also use custom xml files to
            name your own ROIs (assign ID"s) for custom labels. BrainSuite can
            save a label description in .xml format after using the label
            painter tool to create a ROI label. The xml file MUST be in the same
            format as BrainSuite"s label description file (see
            brainsuite_labeldescription.xml for an example). When this flag is
            used, NO 5-digit ROI ID is generated for custom label files and NO
            Statistics will be calculated for ROIs not identified in the custom
            xml file. See also customDiffusionLabel and customT1Label.
            flag: --custom-label-xml %s
    customT1Label: (a file name)
            Same as customDiffusionLabelexcept that the label files specified
            must be in T1 coordinate, i.e. the label files should overlay
            correctly with the T1-weighted scan in BrainSuite. If the trait
            outputDiffusionCoordinates is also used then these input label files
            are also transferred (and saved) to diffusion coordinate for
            statistics in diffusion coordinate. BDP uses nearest-neighborhood
            interpolation for this transformation. See also customLabelXML.
            flag: --custom-t1-label %s
    dataSinkDelay: (a list of items which are a unicode string)
            For use in parallel processing workflows including Brainsuite
            Cortical Surface Extraction sequence. Connect datasink out_file to
            dataSinkDelay to delay execution of BDP until dataSink has finished
            sinking outputs. In particular, BDP may be run after BFC has
            finished. For more information see
            http://brainsuite.org/processing/diffusion/pipeline/
            flag: %s
    dcorrRegMeasure: (u'MI' or u'INVERSION-EPI' or u'INVERSION-T1' or u
             'INVERSION-BOTH' or u'BDP')
            Defines the method for registration-based distortion correction.
            Possible methods are "MI", "INVERSION-EPI", "INVERSION-T1",
            INVERSION-BOTH", and "BDP". MI method uses normalized mutual
            information based cost-function while estimating the distortion
            field. INVERSION-based method uses simpler cost function based on
            sum of squared difference by exploiting the known approximate
            contrast relationship in T1- and T2-weighted images. T2-weighted EPI
            is inverted when INVERSION-EPI is used; T1-image is inverted when
            INVERSION-T1 is used; and both are inverted when INVERSION-BOTH is
            used. BDP method add the MI-based refinement after the correction
            using INVERSION-BOTH method. BDP is the default method when this
            trait is not set.
            flag: --dcorr-reg-method %s
    dcorrWeight: (a float)
            Sets the (scalar) weighting parameter for regularization penalty in
            registration-based distortion correction. Set this trait to a
            single, non-negative number which specifies the weight. A large
            regularization weight encourages smoother distortion field at the
            cost of low measure of image similarity after distortion correction.
            On the other hand, a smaller regularization weight can result into
            higher measure of image similarity but with unrealistic and unsmooth
            distortion field. A weight of 0.5 would reduce the penalty to half
            of the default regularization penalty (By default, this weight is
            set to 1.0). Similarly, a weight of 2.0 would increase the penalty
            to twice of the default penalty.
            flag: --dcorr-regularization-wt %f
    dwiMask: (a file name)
            Specifies the filename of the brain-mask file for diffusion data.
            This mask is used only for co-registration purposes and can affect
            overall quality of co-registration (see t1Mask for definition of
            brain mask for statistics computation). The mask must be a 3D volume
            and should be in the same coordinates as input Diffusion file/data
            (i.e. should overlay correctly with input diffusion data in
            BrainSuite). For best results, the mask should include only brain
            voxels (CSF voxels around brain is also acceptable). When this flag
            is not used, BDP will generate a pseudo mask using first b=0 image
            volume and would save it as fileprefix>.dwi.RSA.mask.nii.gz. In case
            co-registration is not accurate with automatically generated pseudo
            mask, BDP should be re-run with a refined diffusion mask. The mask
            can be generated and/or edited in BrainSuite.
            flag: --dwi-mask %s
    echoSpacing: (a float)
            Sets the echo spacing to t seconds, which is used for fieldmap-based
            distortion correction. This flag is required when using
            fieldmapCorrection
            flag: --echo-spacing=%f
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    estimateODF_3DShore: (a float)
            Estimates ODFs using 3Dshore. Pass in diffusion time, in ms
            flag: --3dshore --diffusion_time_ms %f
    estimateODF_FRACT: (a boolean)
            Estimates ODFs using the Funk-Radon and Cosine Transformation
            (FRACT). The outputs are saved in a separate directory with name
            "FRACT" and the ODFs can be visualized by loading the saved ".odf"
            file in BrainSuite.
            flag: --FRACT
    estimateODF_FRT: (a boolean)
            Estimates ODFs using Funk-Radon Transformation (FRT). The
            coefficient maps for ODFs are saved in a separate directory with
            name "FRT" and the ODFs can be visualized by loading the saved
            ".odf" file in BrainSuite. The derived generalized-FA (GFA) maps are
            also saved in the output directory.
            flag: --FRT
    estimateTensors: (a boolean)
            Estimates diffusion tensors using a weighted log-linear estimation
            and saves derived diffusion tensor parameters (FA, MD, axial,
            radial, L2, L3). This is the default behavior if no diffusion
            modeling flags are specified. The estimated diffusion tensors can be
            visualized by loading the saved *.eig.nii.gz file in BrainSuite. BDP
            reports diffusivity (MD, axial, radial, L2 and L3) in a unit which
            is reciprocal inverse of the unit of input b-value.
            flag: --tensors
    fieldmapCorrection: (a file name)
            Use an acquired fieldmap for distortion correction. The fieldmap
            must have units of radians/second. Specify the filename of the
            fieldmap file. The field of view (FOV) of the fieldmap scan must
            cover the FOV of the diffusion scan. BDP will try to check the
            overlap of the FOV of the two scans and will issue a warning/error
            if the diffusion scan"s FOV is not fully covered by the fieldmap"s
            FOV. BDP uses all of the information saved in the NIfTI header to
            compute the FOV. If you get this error and think that it is
            incorrect, then it can be suppressed using the flag ignore-fieldmap-
            FOV. Neither the image matrix size nor the imaging grid resolution
            of the fieldmap needs to be the same as that of the diffusion scan,
            but the fieldmap must be pre-registred to the diffusion scan. BDP
            does NOT align the fieldmap to the diffusion scan, nor does it check
            the alignment of the fieldmap and diffusion scans. Only NIfTI files
            with extension of .nii or .nii.gz are supported. Fieldmap-based
            distortion correction also requires the echoSpacing. Also
            fieldmapCorrectionMethod allows you to define method for distortion
            correction. least squares is the default method.
            flag: --fieldmap-correction %s
            requires: echoSpacing
    fieldmapCorrectionMethod: (u'pixelshift' or u'leastsq')
            Defines the distortion correction method while using fieldmap.
            Possible methods are "pixelshift" and "leastsq". leastsq is the
            default method when this flag is not used. Pixel-shift (pixelshift)
            method uses image interpolation to un-distort the distorted
            diffusion images. Least squares (leastsq) method uses a physical
            model of distortion which is more accurate (and more computationally
            expensive) than pixel-shift method.
            flag: --fieldmap-correction-method %s
            mutually_exclusive: skipIntensityCorr
    fieldmapSmooth: (a float)
            Applies 3D Gaussian smoothing with a standard deviation of S
            millimeters (mm) to the input fieldmap before applying distortion
            correction. This trait is only useful with fieldmapCorrection. Skip
            this trait for no smoothing.
            flag: --fieldmap-smooth3=%f
    flagConfigFile: (a file name)
            Uses the defined file to specify BDP flags which can be useful for
            batch processing. A flag configuration file is a plain text file
            which can contain any number of BDP"s optional flags (and their
            parameters) separated by whitespace. Everything coming after # until
            end-of-line is treated as comment and is ignored. If a flag is
            defined in configuration file and is also specified in the command
            used to run BDP, then the later get preference and overrides the
            definition in configuration file.
            flag: --flag-conf-file %s
    forcePartialROIStats: (a boolean)
            The field of view (FOV) of the diffusion and T1-weighted scans may
            differ significantly in some situations. This may result in partial
            acquisitions of some ROIs in the diffusion scan. By default, BDP
            does not compute statistics for partially acquired ROIs and shows
            warnings. This flag forces computation of statistics for all ROIs,
            including those which are partially acquired. When this flag is
            used, number of missing voxels are also reported for each ROI in
            statistics files. Number of missing voxels are reported in the same
            coordinate system as the statistics file.
            flag: --force-partial-roi-stats
    generateStats: (a boolean)
            Generate ROI-wise statistics of estimated diffusion tensor
            parameters. Units of the reported statistics are same as that of the
            estimated tensor parameters (see estimateTensors). Mean, variance,
            and voxel counts of white matter(WM), grey matter(GM), and both WM
            and GM combined are written for each estimated parameter in a
            separate comma-seperated value csv) file. BDP uses the ROI labels
            generated by Surface-Volume Registration (SVReg) in the BrainSuite
            extraction sequence. Specifically, it looks for labels saved in
            either fileprefix>.svreg.corr.label.nii.gz or
            <fileprefix>.svreg.label.nii.gz. In case both files are present,
            only the first file is used. Also see customDiffusionLabel and
            customT1Label for specifying your own ROIs. It is also possible to
            forgo computing the SVReg ROI-wise statistics and only compute stats
            with custom labels if SVReg label is missing. BDP also transfers
            (and saves) the label/mask files to appropriate coordinates before
            computing statistics. Also see outputDiffusionCoordinates for
            outputs in diffusion coordinate and forcePartialROIStats for an
            important note about field of view of diffusion and T1-weighted
            scans.
            flag: --generate-stats
    ignoreFieldmapFOV: (a boolean)
            Supresses the error generated by an insufficient field of view of
            the input fieldmap and continues with the processing. It is useful
            only when used with fieldmap-based distortion correction. See
            fieldmap-correction for a detailed explanation.
            flag: --ignore-fieldmap-fov
    ignoreMemory: (a boolean)
            Deactivates the inbuilt memory checks and forces BDP to run
            registration-based distortion correction at its default resolution
            even on machines with a low amount of memory. This may result in an
            out-of-memory error when BDP cannot allocate sufficient memory.
            flag: --ignore-memory
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    lowMemory: (a boolean)
            Activates low-memory mode. This will run the registration-based
            distortion correction at a lower resolution, which could result in a
            less-accurate correction. This should only be used when no other
            alternative is available.
            flag: --low-memory
    odfLambta: (a boolean)
            Sets the regularization parameter, lambda, of the Laplace-Beltrami
            operator while estimating ODFs. The default value is set to 0.006 .
            This can be used to set the appropriate regularization for the input
            diffusion data.
            flag: --odf-lambda <L>
    onlyStats: (a boolean)
            Skip all of the processing (co-registration, distortion correction
            and tensor/ODF estimation) and directly start computation of
            statistics. This flag is useful when BDP was previously run on a
            subject (or fileprefix>) and statistics need to be (re-)computed
            later. This assumes that all the necessary files were generated
            earlier. All of the other flags MUST be used in the same way as they
            were in the initial BDP run that processed the data.
            flag: --generate-only-stats
    outPrefix: (a unicode string)
            Specifies output fileprefix when noStructuralRegistration is used.
            The fileprefix can not start with a dash (-) and should be a simple
            string reflecting the absolute path to desired location, along with
            outPrefix. When this flag is not specified (and
            noStructuralRegistration is used) then the output files have same
            file-base as the input diffusion file. This trait is ignored when
            noStructuralRegistration is not used.
            flag: --output-fileprefix %s
    outputDiffusionCoordinates: (a boolean)
            Enables estimation of diffusion tensors and/or ODFs (and statistics
            if applicable) in the native diffusion coordinate in addition to the
            default T1-coordinate. All native diffusion coordinate files are
            saved in a seperate folder named "diffusion_coord_outputs". In case
            statistics computation is required, it will also transform/save all
            label/mask files required to diffusion coordinate (see generateStats
            for details).
            flag: --output-diffusion-coordinate
    outputSubdir: (a unicode string)
            By default, BDP writes out all the output (and intermediate) files
            in the same directory (or folder) as the BFC file. This flag allows
            to specify a sub-directory name in which output (and intermediate)
            files would be written. BDP will create the sub-directory in the
            same directory as BFC file. <directory_name> should be the name of
            the sub-directory without any path. This can be useful to organize
            all outputs generated by BDP in a separate sub-directory.
            flag: --output-subdir %s
    phaseEncodingDirection: (u'x' or u'x-' or u'y' or u'y-' or u'z' or
             u'z-')
            Specifies the phase-encoding direction of the EPI (diffusion)
            images. It is same as the dominant direction of distortion in the
            images. This information is used to constrain the distortion
            correction along the specified direction. Directions are represented
            by any one of x, x-, y, y-, z or z-. "x" direction increases towards
            the right side of the subject, while "x-" increases towards the left
            side of the subject. Similarly, "y" and "y-" are along the anterior-
            posterior direction of the subject, and "z" & "z-" are along the
            inferior-superior direction. When this flag is not used, BDP uses
            "y" as the default phase-encoding direction.
            flag: --dir=%s
    rigidRegMeasure: (u'MI' or u'INVERSION' or u'BDP')
            Defines the similarity measure to be used for rigid registration.
            Possible measures are "MI", "INVERSION" and "BDP". MI measure uses
            normalized mutual information based cost function. INVERSION measure
            uses simpler cost function based on sum of squared difference by
            exploiting the approximate inverse-contrast relationship in T1- and
            T2-weighted images. BDP measure combines MI and INVERSION. It starts
            with INVERSION measure and refines the result with MI measure. BDP
            is the default measure when this trait is not set.
            flag: --rigid-reg-measure %s
    skipDistortionCorr: (a boolean)
            Skips distortion correction completely and performs only a rigid
            registration of diffusion and T1-weighted image. This can be useful
            when the input diffusion images do not have any distortion or they
            have been corrected for distortion.
            flag: --no-distortion-correction
    skipIntensityCorr: (a boolean)
            Disables intensity correction when performing distortion correction.
            Intensity correction can change the noise distribution in the
            corrected image, but it does not affect estimated diffusion
            parameters like FA, etc.
            flag: --no-intensity-correction
            mutually_exclusive: fieldmapCorrectionMethod
    skipNonuniformityCorr: (a boolean)
            Skips intensity non-uniformity correction in b=0 image for
            registration-based distortion correction. The intensity non-
            uniformity correction does not affect any diffusion modeling.
            flag: --no-nonuniformity-correction
    t1Mask: (a file name)
            Specifies the filename of the brain-mask file for input T1-weighted
            image. This mask can be same as the brain mask generated during
            BrainSuite extraction sequence. For best results, the mask should
            not include any extra-meningial tissues from T1-weighted image. The
            mask must be in the same coordinates as input T1-weighted image
            (i.e. should overlay correctly with input <fileprefix>.bfc.nii.gz
            file in BrainSuite). This mask is used for co-registration and
            defining brain boundary for statistics computation. The mask can be
            generated and/or edited in BrainSuite. In case
            outputDiffusionCoordinates is also used, this mask is first
            transformed to diffusion coordinate and the transformed mask is used
            for defining brain boundary in diffusion coordinates. When t1Mask is
            not set, BDP will try to use fileprefix>.mask.nii.gz as brain-mask.
            If <fileprefix>.mask.nii.gz is not found, then BDP will use the
            input <fileprefix>.bfc.nii.gz itself as mask (i.e. all non-zero
            voxels in <fileprefix>.bfc.nii.gz is assumed to constitute brain
            mask).
            flag: --t1-mask %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    threads: (an integer (int or long))
            Sets the number of parallel process threads which can be used for
            computations to N, where N must be an integer. Default value of N is
            flag: --threads=%d
    transformDataOnly: (a boolean)
            Skip all of the processing (co-registration, distortion correction
            and tensor/ODF estimation) and directly start transformation of
            defined custom volumes, mask and labels (using transformT1Volume,
            transformDiffusionVolume, transformT1Surface,
            transformDiffusionSurface, customDiffusionLabel, customT1Label).
            This flag is useful when BDP was previously run on a subject (or
            <fileprefix>) and some more data (volumes, mask or labels) need to
            be transformed across the T1-diffusion coordinate spaces. This
            assumes that all the necessary files were generated earlier and all
            of the other flags MUST be used in the same way as they were in the
            initial BDP run that processed the data.
            flag: --transform-data-only
    transformDiffusionSurface: (a file name)
            Same as transformT1Volume, except that the .dfs files specified must
            be in diffusion coordinate, i.e. the surface files should overlay
            correctly with the diffusion scan in BrainSuite. The transformed
            files are written to the output directory with suffix ".T1_coord" in
            the filename. See also transformT1Volume.
            flag: --transform-diffusion-surface %s
    transformDiffusionVolume: (a file name)
            This flag allows to define custom volumes in diffusion coordinate
            which would be transformed into T1 coordinate in a rigid fashion.
            The flag must be followed by the name of either a NIfTI file or of a
            folder that contains one or more NIfTI files. All of the files must
            be in diffusion coordinate, i.e. the files should overlay correctly
            with the diffusion scan in BrainSuite. Only NIfTI files with an
            extension of .nii or .nii.gz are supported. The transformed files
            are written to the output directory with suffix ".T1_coord" in the
            filename and will not be corrected for distortion, if any. The trait
            transformInterpolation can be used to define the type of
            interpolation that would be used (default is set to linear). If you
            are attempting to transform a label file or mask file, use "nearest"
            interpolation method with transformInterpolation. See also
            transformT1Volume and transformInterpolation
            flag: --transform-diffusion-volume %s
    transformInterpolation: (u'linear' or u'nearest' or u'cubic' or
             u'spline')
            Defines the type of interpolation method which would be used while
            transforming volumes defined by transformT1Volume and
            transformDiffusionVolume. Possible methods are "linear", "nearest",
            "cubic" and "spline". By default, "linear" interpolation is used.
            flag: --transform-interpolation %s
    transformT1Surface: (a file name)
            Similar to transformT1Volume, except that this flag allows
            transforming surfaces (instead of volumes) in T1 coordinate into
            diffusion coordinate in a rigid fashion. The flag must be followed
            by the name of either a .dfs file or of a folder that contains one
            or more dfs files. All of the files must be in T1 coordinate, i.e.
            the files should overlay correctly with the T1-weighted scan in
            BrainSuite. The transformed files are written to the output
            directory with suffix D_coord" in the filename.
            flag: --transform-t1-surface %s
    transformT1Volume: (a file name)
            Same as transformDiffusionVolume except that files specified must be
            in T1 coordinate, i.e. the files should overlay correctly with the
            input <fileprefix>.bfc.nii.gz files in BrainSuite. BDP transforms
            these data/images from T1 coordinate to diffusion coordinate. The
            transformed files are written to the output directory with suffix
            ".D_coord" in the filename. See also transformDiffusionVolume and
            transformInterpolation.
            flag: --transform-t1-volume %s

Outputs:

    None

## Bfc

Link to code

Wraps command bfc

bias field corrector (BFC) This program corrects gain variation in T1-weighted MRI.

http://brainsuite.org/processing/surfaceextraction/bfc/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> bfc = brainsuite.Bfc()
    >>> bfc.inputs.inputMRIFile = example_data('structural.nii')
    >>> bfc.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = bfc.run()

Inputs:

    [Mandatory]
    inputMRIFile: (a file name)
            input skull-stripped MRI volume
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    biasEstimateConvergenceThreshold: (a float)
            bias estimate convergence threshold (values > 0.1 disable)
            flag: --beps %f
    biasEstimateSpacing: (an integer (int or long))
            bias sample spacing (voxels)
            flag: -s %d
    biasFieldEstimatesOutputPrefix: (a unicode string)
            save iterative bias field estimates as <prefix>.n.field.nii.gz
            flag: --biasprefix %s
    biasRange: (u'low' or u'medium' or u'high')
            Preset options for bias_model
             low: small bias model [0.95,1.05]
            medium: medium bias model [0.90,1.10]
             high: high bias model [0.80,1.20]
            flag: %s
    controlPointSpacing: (an integer (int or long))
            control point spacing (voxels)
            flag: -c %d
    convergenceThreshold: (a float)
            convergence threshold
            flag: --eps %f
    correctWholeVolume: (a boolean)
            apply correction field to entire volume
            flag: --extrapolate
    correctedImagesOutputPrefix: (a unicode string)
            save iterative corrected images as <prefix>.n.bfc.nii.gz
            flag: --prefix %s
    correctionScheduleFile: (a file name)
            list of parameters
            flag: --schedule %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    histogramRadius: (an integer (int or long))
            histogram radius (voxels)
            flag: -r %d
    histogramType: (u'ellipse' or u'block')
            Options for type of histogram
            ellipse: use ellipsoid for ROI histogram
            block :use block for ROI histogram
            flag: %s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    inputMaskFile: (a file name)
            mask file
            flag: -m %s
    intermediate_file_type: (u'analyze' or u'nifti' or u'gzippedAnalyze'
             or u'gzippedNifti')
            Options for the format in which intermediate files are generated
            flag: %s
    iterativeMode: (a boolean)
            iterative mode (overrides -r, -s, -c, -w settings)
            flag: --iterate
    maxBias: (a float, nipype default value: 1.5)
            maximum allowed bias value
            flag: -U %f
    minBias: (a float, nipype default value: 0.5)
            minimum allowed bias value
            flag: -L %f
    outputBiasField: (a file name)
            save bias field estimate
            flag: --bias %s
    outputMRIVolume: (a file name)
            output bias-corrected MRI volume.If unspecified, output file name
            will be auto generated.
            flag: -o %s
    outputMaskedBiasField: (a file name)
            save bias field estimate (masked)
            flag: --maskedbias %s
    splineLambda: (a float)
            spline stiffness weighting parameter
            flag: -w %f
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            display timing information
            flag: --timer
    verbosityLevel: (an integer (int or long))
            verbosity level (0=silent)
            flag: -v %d

Outputs:

    correctionScheduleFile: (a file name)
            path/name of schedule file
    outputBiasField: (a file name)
            path/name of bias field output file
    outputMRIVolume: (a file name)
            path/name of output file
    outputMaskedBiasField: (a file name)
            path/name of masked bias field output

## Bse

Link to code

Wraps command bse

brain surface extractor (BSE) This program performs automated skull and scalp removal on T1-weighted MRI volumes.

http://brainsuite.org/processing/surfaceextraction/bse/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> bse = brainsuite.Bse()
    >>> bse.inputs.inputMRIFile = example_data('structural.nii')
    >>> results = bse.run()

Inputs:

    [Mandatory]
    inputMRIFile: (a file name)
            input MRI volume
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    diffusionConstant: (a float, nipype default value: 25)
            diffusion constant
            flag: -d %f
    diffusionIterations: (an integer (int or long), nipype default value:
             3)
            diffusion iterations
            flag: -n %d
    dilateFinalMask: (a boolean, nipype default value: True)
            dilate final mask
            flag: -p
    edgeDetectionConstant: (a float, nipype default value: 0.64)
            edge detection constant
            flag: -s %f
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    noRotate: (a boolean)
            retain original orientation(default behavior will auto-rotate input
            NII files to LPI orientation)
            flag: --norotate
    outputCortexFile: (a file name)
            cortex file
            flag: --cortex %s
    outputDetailedBrainMask: (a file name)
            save detailed brain mask
            flag: --hires %s
    outputDiffusionFilter: (a file name)
            diffusion filter output
            flag: --adf %s
    outputEdgeMap: (a file name)
            edge map output
            flag: --edge %s
    outputMRIVolume: (a file name)
            output brain-masked MRI volume. If unspecified, output file name
            will be auto generated.
            flag: -o %s
    outputMaskFile: (a file name)
            save smooth brain mask. If unspecified, output file name will be
            auto generated.
            flag: --mask %s
    radius: (a float, nipype default value: 1)
            radius of erosion/dilation filter
            flag: -r %f
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            show timing
            flag: --timer
    trim: (a boolean, nipype default value: True)
            trim brainstem
            flag: --trim
    verbosityLevel: (a float, nipype default value: 1)
             verbosity level (0=silent)
            flag: -v %f

Outputs:

    outputCortexFile: (a file name)
            path/name of cortex file
    outputDetailedBrainMask: (a file name)
            path/name of detailed brain mask
    outputDiffusionFilter: (a file name)
            path/name of diffusion filter output
    outputEdgeMap: (a file name)
            path/name of edge map output
    outputMRIVolume: (a file name)
            path/name of brain-masked MRI volume
    outputMaskFile: (a file name)
            path/name of smooth brain mask

## Cerebro

Link to code

Wraps command cerebro

Cerebrum/cerebellum labeling tool This program performs automated labeling of cerebellum and cerebrum in T1 MRI. Input MRI should be skull-stripped or a brain-only mask should be provided.

http://brainsuite.org/processing/surfaceextraction/cerebrum/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> cerebro = brainsuite.Cerebro()
    >>> cerebro.inputs.inputMRIFile = example_data('structural.nii')
    >>> cerebro.inputs.inputAtlasMRIFile = 'atlasMRIVolume.img'
    >>> cerebro.inputs.inputAtlasLabelFile = 'atlasLabels.img'
    >>> cerebro.inputs.inputBrainMaskFile = example_data('mask.nii')
    >>> results = cerebro.run()

Inputs:

    [Mandatory]
    inputAtlasLabelFile: (a file name)
            atlas labeling
            flag: --atlaslabels %s
    inputAtlasMRIFile: (a file name)
            atlas MRI volume
            flag: --atlas %s
    inputMRIFile: (a file name)
            input 3D MRI volume
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    costFunction: (an integer (int or long), nipype default value: 2)
            0,1,2
            flag: -c %d
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    inputBrainMaskFile: (a file name)
            brain mask file
            flag: -m %s
    keepTempFiles: (a boolean)
            don't remove temporary files
            flag: --keep
    linearConvergence: (a float)
            linear convergence
            flag: --linconv %f
    outputAffineTransformFile: (a file name)
            save affine transform to file.
            flag: --air %s
    outputCerebrumMaskFile: (a file name)
            output cerebrum mask volume. If unspecified, output file name will
            be auto generated.
            flag: -o %s
    outputLabelVolumeFile: (a file name)
            output labeled hemisphere/cerebrum volume. If unspecified, output
            file name will be auto generated.
            flag: -l %s
    outputWarpTransformFile: (a file name)
            save warp transform to file.
            flag: --warp %s
    tempDirectory: (a unicode string)
            specify directory to use for temporary files
            flag: --tempdir %s
    tempDirectoryBase: (a unicode string)
            create a temporary directory within this directory
            flag: --tempdirbase %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    useCentroids: (a boolean)
            use centroids of data to initialize position
            flag: --centroids
    verbosity: (an integer (int or long))
            verbosity level (0=silent)
            flag: -v %d
    warpConvergence: (a float)
            warp convergence
            flag: --warpconv %f
    warpLabel: (an integer (int or long))
            warp order (2,3,4,5,6,7,8)
            flag: --warplevel %d

Outputs:

    outputAffineTransformFile: (a file name)
            path/name of affine transform file
    outputCerebrumMaskFile: (a file name)
            path/name of cerebrum mask file
    outputLabelVolumeFile: (a file name)
            path/name of label mask file
    outputWarpTransformFile: (a file name)
            path/name of warp transform file

## Cortex

Link to code

Wraps command cortex

cortex extractor This program produces a cortical mask using tissue fraction estimates and a co-registered cerebellum/hemisphere mask.

http://brainsuite.org/processing/surfaceextraction/cortex/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> cortex = brainsuite.Cortex()
    >>> cortex.inputs.inputHemisphereLabelFile = example_data('mask.nii')
    >>> cortex.inputs.inputTissueFractionFile = example_data('tissues.nii.gz')
    >>> results = cortex.run()

Inputs:

    [Mandatory]
    inputHemisphereLabelFile: (a file name)
            hemisphere / lobe label volume
            flag: -h %s
    inputTissueFractionFile: (a file name)
            tissue fraction file (32-bit float)
            flag: -f %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    computeGCBoundary: (a boolean)
            compute GM/CSF boundary
            flag: -g
    computeWGBoundary: (a boolean, nipype default value: True)
            compute WM/GM boundary
            flag: -w
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    includeAllSubcorticalAreas: (a boolean, nipype default value: True)
            include all subcortical areas in WM mask
            flag: -a
    outputCerebrumMask: (a file name)
            output structure mask. If unspecified, output file name will be auto
            generated.
            flag: -o %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            timing function
            flag: --timer
    tissueFractionThreshold: (a float, nipype default value: 50.0)
            tissue fraction threshold (percentage)
            flag: -p %f
    verbosity: (an integer (int or long))
            verbosity level
            flag: -v %d

Outputs:

    outputCerebrumMask: (a file name)
            path/name of cerebrum mask

## Dewisp

Link to code

Wraps command dewisp

dewisp removes wispy tendril structures from cortex model binary masks. It does so based on graph theoretic analysis of connected components, similar to TCA. Each branch of the structure graph is analyzed to determine pinch points that indicate a likely error in segmentation that attaches noise to the image. The pinch threshold determines how many voxels the cross-section can be before it is considered part of the image.

http://brainsuite.org/processing/surfaceextraction/dewisp/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> dewisp = brainsuite.Dewisp()
    >>> dewisp.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = dewisp.run()

Inputs:

    [Mandatory]
    inputMaskFile: (a file name)
            input file
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    maximumIterations: (an integer (int or long))
            maximum number of iterations
            flag: -n %d
    outputMaskFile: (a file name)
            output file. If unspecified, output file name will be auto
            generated.
            flag: -o %s
    sizeThreshold: (an integer (int or long))
            size threshold
            flag: -t %d
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            time processing
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity
            flag: -v %d

Outputs:

    outputMaskFile: (a file name)
            path/name of mask file

## Dfs

Link to code

Wraps command dfs

Surface Generator Generates mesh surfaces using an isosurface algorithm.

http://brainsuite.org/processing/surfaceextraction/inner-cortical-surface/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> dfs = brainsuite.Dfs()
    >>> dfs.inputs.inputVolumeFile = example_data('structural.nii')
    >>> results = dfs.run()

Inputs:

    [Mandatory]
    inputVolumeFile: (a file name)
            input 3D volume
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    curvatureWeighting: (a float, nipype default value: 5.0)
            curvature weighting
            flag: -w %f
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    inputShadingVolume: (a file name)
            shade surface model with data from image volume
            flag: -c %s
    noNormalsFlag: (a boolean)
            do not compute vertex normals
            flag: --nonormals
    nonZeroTessellation: (a boolean)
            tessellate non-zero voxels
            flag: -nz
            mutually_exclusive: nonZeroTessellation, specialTessellation
    outputSurfaceFile: (a file name)
            output surface mesh file. If unspecified, output file name will be
            auto generated.
            flag: -o %s
    postSmoothFlag: (a boolean)
            smooth vertices after coloring
            flag: --postsmooth
    scalingPercentile: (a float)
            scaling percentile
            flag: -f %f
    smoothingConstant: (a float, nipype default value: 0.5)
            smoothing constant
            flag: -a %f
    smoothingIterations: (an integer (int or long), nipype default value:
             10)
            number of smoothing iterations
            flag: -n %d
    specialTessellation: (u'greater_than' or u'less_than' or u'equal_to')
            To avoid throwing a UserWarning, set tessellationThreshold first.
            Then set this attribute.
            Usage: tessellate voxels greater_than, less_than, or equal_to
            <tessellationThreshold>
            flag: %s, position: -1
            mutually_exclusive: nonZeroTessellation, specialTessellation
            requires: tessellationThreshold
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    tessellationThreshold: (a float)
            To be used with specialTessellation. Set this value first, then set
            specialTessellation value.
            Usage: tessellate voxels greater_than, less_than, or equal_to
            <tessellationThreshold>
            flag: %f
    timer: (a boolean)
            timing function
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity (0 = quiet)
            flag: -v %d
    zeroPadFlag: (a boolean)
            zero-pad volume (avoids clipping at edges)
            flag: -z

Outputs:

    outputSurfaceFile: (a file name)
            path/name of surface file

## Hemisplit

Link to code

Wraps command hemisplit

Hemisphere splitter Splits a surface object into two separate surfaces given an input label volume. Each vertex is labeled left or right based on the labels being odd (left) or even (right). The largest contour on the split surface is then found and used as the separation between left and right.

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> hemisplit = brainsuite.Hemisplit()
    >>> hemisplit.inputs.inputSurfaceFile = 'input_surf.dfs'
    >>> hemisplit.inputs.inputHemisphereLabelFile = 'label.nii'
    >>> hemisplit.inputs.pialSurfaceFile = 'pial.dfs'
    >>> results = hemisplit.run()

Inputs:

    [Mandatory]
    inputHemisphereLabelFile: (a file name)
            input hemisphere label volume
            flag: -l %s
    inputSurfaceFile: (a file name)
            input surface
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    outputLeftHemisphere: (a file name)
            output surface file, left hemisphere. If unspecified, output file
            name will be auto generated.
            flag: --left %s
    outputLeftPialHemisphere: (a file name)
            output pial surface file, left hemisphere. If unspecified, output
            file name will be auto generated.
            flag: -pl %s
    outputRightHemisphere: (a file name)
            output surface file, right hemisphere. If unspecified, output file
            name will be auto generated.
            flag: --right %s
    outputRightPialHemisphere: (a file name)
            output pial surface file, right hemisphere. If unspecified, output
            file name will be auto generated.
            flag: -pr %s
    pialSurfaceFile: (a file name)
            pial surface file -- must have same geometry as input surface
            flag: -p %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            timing function
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity (0 = silent)
            flag: -v %d

Outputs:

    outputLeftHemisphere: (a file name)
            path/name of left hemisphere
    outputLeftPialHemisphere: (a file name)
            path/name of left pial hemisphere
    outputRightHemisphere: (a file name)
            path/name of right hemisphere
    outputRightPialHemisphere: (a file name)
            path/name of right pial hemisphere

## Pialmesh

Link to code

Wraps command pialmesh

pialmesh computes a pial surface model using an inner WM/GM mesh and a tissue fraction map.

http://brainsuite.org/processing/surfaceextraction/pial/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> pialmesh = brainsuite.Pialmesh()
    >>> pialmesh.inputs.inputSurfaceFile = 'input_mesh.dfs'
    >>> pialmesh.inputs.inputTissueFractionFile = 'frac_file.nii.gz'
    >>> pialmesh.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = pialmesh.run()

Inputs:

    [Mandatory]
    inputMaskFile: (a file name)
            restrict growth to mask file region
            flag: -m %s
    inputSurfaceFile: (a file name)
            input file
            flag: -i %s
    inputTissueFractionFile: (a file name)
            floating point (32) tissue fraction image
            flag: -f %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    exportPrefix: (a unicode string)
            prefix for exporting surfaces if interval is set
            flag: --prefix %s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    laplacianSmoothing: (a float, nipype default value: 0.025)
            apply Laplacian smoothing
            flag: --smooth %f
    maxThickness: (a float, nipype default value: 20)
            maximum allowed tissue thickness
            flag: --max %f
    normalSmoother: (a float, nipype default value: 0.2)
            strength of normal smoother.
            flag: --nc %f
    numIterations: (an integer (int or long), nipype default value: 100)
            number of iterations
            flag: -n %d
    outputInterval: (an integer (int or long), nipype default value: 10)
            output interval
            flag: --interval %d
    outputSurfaceFile: (a file name)
            output file. If unspecified, output file name will be auto
            generated.
            flag: -o %s
    recomputeNormals: (a boolean)
            recompute normals at each iteration
            flag: --norm
    searchRadius: (a float, nipype default value: 1)
            search radius
            flag: -r %f
    stepSize: (a float, nipype default value: 0.4)
            step size
            flag: -s %f
    tangentSmoother: (a float)
            strength of tangential smoother.
            flag: --tc %f
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            show timing
            flag: --timer
    tissueThreshold: (a float, nipype default value: 1.05)
            tissue threshold
            flag: -t %f
    verbosity: (an integer (int or long))
            verbosity
            flag: -v %d

Outputs:

    outputSurfaceFile: (a file name)
            path/name of surface file

## Pvc

Link to code

Wraps command pvc

partial volume classifier (PVC) tool. This program performs voxel-wise tissue classification T1-weighted MRI. Image should be skull-stripped and bias-corrected before tissue classification.

http://brainsuite.org/processing/surfaceextraction/pvc/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> pvc = brainsuite.Pvc()
    >>> pvc.inputs.inputMRIFile = example_data('structural.nii')
    >>> pvc.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = pvc.run()

Inputs:

    [Mandatory]
    inputMRIFile: (a file name)
            MRI file
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    inputMaskFile: (a file name)
            brain mask file
            flag: -m %s
    outputLabelFile: (a file name)
            output label file. If unspecified, output file name will be auto
            generated.
            flag: -o %s
    outputTissueFractionFile: (a file name)
            output tissue fraction file
            flag: -f %s
    spatialPrior: (a float)
            spatial prior strength
            flag: -l %f
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    threeClassFlag: (a boolean)
            use a three-class (CSF=0,GM=1,WM=2) labeling
            flag: -3
    timer: (a boolean)
            time processing
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity level (0 = silent)
            flag: -v %d

Outputs:

    outputLabelFile: (a file name)
            path/name of label file
    outputTissueFractionFile: (a file name)
            path/name of tissue fraction file

## SVReg

Link to code

Wraps command svreg.sh

surface and volume registration (svreg) This program registers a subject’s BrainSuite-processed volume and surfaces to an atlas, allowing for automatic labelling of volume and surface ROIs.

For more information, please see: http://brainsuite.org/processing/svreg/usage/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> svreg = brainsuite.SVReg()
    >>> svreg.inputs.subjectFilePrefix = 'home/user/btestsubject/testsubject'
    >>> svreg.inputs.refineOutputs = True
    >>> svreg.inputs.skipToVolumeReg = False
    >>> svreg.inputs. keepIntermediates = True
    >>> svreg.inputs.verbosity2 = True
    >>> svreg.inputs.displayTimestamps = True
    >>> svreg.inputs.useSingleThreading = True
    >>> results = svreg.run()

Inputs:

    [Mandatory]
    subjectFilePrefix: (a unicode string)
            Absolute path and filename prefix of the subjects output from
            BrainSuite Cortical Surface Extraction Sequence
            flag: '%s', position: 0

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    atlasFilePrefix: (a unicode string)
            Optional: Absolute Path and filename prefix of atlas files and
            labels to which the subject will be registered. If unspecified,
            SVRegwill use its own included atlas files
            flag: '%s', position: 1
    curveMatchingInstructions: (a unicode string)
            Used to take control of the curve matching process between the atlas
            and subject. One can specify the name of the .dfc file <sulname.dfc>
            and the sulcal numbers <#sul> to be used as constraints. example:
            curveMatchingInstructions = "subbasename.right.dfc 1 2 20"
            flag: '-cur %s'
    dataSinkDelay: (a list of items which are a unicode string)
            Connect datasink out_file to dataSinkDelay to delay execution of
            SVReg until dataSink has finished sinking CSE outputs.For use with
            parallel processing workflows including Brainsuites Cortical Surface
            Extraction sequence (SVReg requires certain files from Brainsuite
            CSE, which must all be in the pathway specified by
            subjectFilePrefix. see http://brainsuite.org/processing/svreg/usage/
            for list of required inputs
            flag: %s
    displayModuleName: (a boolean)
            Module name will be displayed in the messages
            flag: '-m'
    displayTimestamps: (a boolean)
            Timestamps will be displayed in the messages
            flag: '-t'
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    iterations: (an integer (int or long))
            Assigns a number of iterations in the intensity registration step.if
            unspecified, performs 100 iterations
            flag: '-H %d'
    keepIntermediates: (a boolean)
            Keep the intermediate files after the svreg sequence is complete.
            flag: '-k'
    pialSurfaceMaskDilation: (an integer (int or long))
            Cortical volume labels found in file output
            subbasename.svreg.label.nii.gz find its boundaries by using the pial
            surface then dilating by 1 voxel. Use this flag in order to control
            the number of pial surface mask dilation. (ie. -D 0 will assign no
            voxel dilation)
            flag: '-D %d'
    refineOutputs: (a boolean)
            Refine outputs at the expense of more processing time.
            flag: '-r'
    shortMessages: (a boolean)
            Short messages instead of detailed messages
            flag: '-gui'
    skipToIntensityReg: (a boolean)
            If the p-harmonic volumetric registration was already performed at
            an earlier time and the user would not like to redo this step, then
            this flag may be used to skip ahead to the intensity registration
            and label transfer step.
            flag: '-p'
    skipToVolumeReg: (a boolean)
            If surface registration was already performed at an earlier time and
            the user would not like to redo this step, then this flag may be
            used to skip ahead to the volumetric registration. Necessary input
            files will need to be present in the input directory called by the
            command.
            flag: '-s'
    skipVolumetricProcessing: (a boolean)
            Only surface registration and labeling will be performed. Volumetric
            processing will be skipped.
            flag: '-S'
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    useCerebrumMask: (a boolean)
            The cerebrum mask <subbasename.cerebrum.mask.nii.gz> will be used
            for masking the final labels instead of the default pial surface
            mask. Every voxel will be labeled within the cerebrum mask
            regardless of the boundaries of the pial surface.
            flag: '-C'
    useManualMaskFile: (a boolean)
            Can call a manually edited cerebrum mask to limit boundaries. Will
            use file: subbasename.cerebrum.mask.nii.gz Make sure to correctly
            replace your manually edited mask file in your input folder with the
            correct subbasename.
            flag: '-cbm'
    useMultiThreading: (a boolean)
            If multiple CPUs are present on the system, the code will try to use
            multithreading to make the execution fast.
            flag: '-P'
    useSingleThreading: (a boolean)
            Use single threaded mode.
            flag: '-U'
    verbosity0: (a boolean)
            no messages will be reported
            flag: '-v0'
            mutually_exclusive: verbosity0, verbosity1, verbosity2
    verbosity1: (a boolean)
            messages will be reported but not the iteration-wise detailed
            messages
            flag: '-v1'
            mutually_exclusive: verbosity0, verbosity1, verbosity2
    verbosity2: (a boolean)
            all the messages, including per-iteration, will be displayed
            flag: 'v2'
            mutually_exclusive: verbosity0, verbosity1, verbosity2

Outputs:

    None

## Scrubmask

Link to code

Wraps command scrubmask

ScrubMask tool scrubmask filters binary masks to trim loosely connected voxels that may result from segmentation errors and produce bumps on tessellated surfaces.

http://brainsuite.org/processing/surfaceextraction/scrubmask/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> scrubmask = brainsuite.Scrubmask()
    >>> scrubmask.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = scrubmask.run()

Inputs:

    [Mandatory]
    inputMaskFile: (a file name)
            input structure mask file
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    backgroundFillThreshold: (an integer (int or long), nipype default
             value: 2)
            background fill threshold
            flag: -b %d
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    foregroundTrimThreshold: (an integer (int or long), nipype default
             value: 0)
            foreground trim threshold
            flag: -f %d
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    numberIterations: (an integer (int or long))
            number of iterations
            flag: -n %d
    outputMaskFile: (a file name)
            output structure mask file. If unspecified, output file name will be
            auto generated.
            flag: -o %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            timing function
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity (0=silent)
            flag: -v %d

Outputs:

    outputMaskFile: (a file name)
            path/name of mask file

## Skullfinder

Link to code

Wraps command skullfinder

Skull and scalp segmentation algorithm.

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> skullfinder = brainsuite.Skullfinder()
    >>> skullfinder.inputs.inputMRIFile = example_data('structural.nii')
    >>> skullfinder.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = skullfinder.run()

Inputs:

    [Mandatory]
    inputMRIFile: (a file name)
            input file
            flag: -i %s
    inputMaskFile: (a file name)
            A brain mask file, 8-bit image (0=non-brain, 255=brain)
            flag: -m %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    bgLabelValue: (an integer (int or long))
            background label value (0-255)
            flag: --bglabel %d
    brainLabelValue: (an integer (int or long))
            brain label value (0-255)
            flag: --brainlabel %d
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    lowerThreshold: (an integer (int or long))
            Lower threshold for segmentation
            flag: -l %d
    outputLabelFile: (a file name)
            output multi-colored label volume segmenting brain, scalp, inner
            skull & outer skull If unspecified, output file name will be auto
            generated.
            flag: -o %s
    performFinalOpening: (a boolean)
            perform a final opening operation on the scalp mask
            flag: --finalOpening
    scalpLabelValue: (an integer (int or long))
            scalp label value (0-255)
            flag: --scalplabel %d
    skullLabelValue: (an integer (int or long))
            skull label value (0-255)
            flag: --skulllabel %d
    spaceLabelValue: (an integer (int or long))
            space label value (0-255)
            flag: --spacelabel %d
    surfaceFilePrefix: (a unicode string)
            if specified, generate surface files for brain, skull, and scalp
            flag: -s %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    upperThreshold: (an integer (int or long))
            Upper threshold for segmentation
            flag: -u %d
    verbosity: (an integer (int or long))
            verbosity
            flag: -v %d

Outputs:

    outputLabelFile: (a file name)
            path/name of label file

## Tca

Link to code

Wraps command tca

topological correction algorithm (TCA) This program removes topological handles from a binary object.

http://brainsuite.org/processing/surfaceextraction/tca/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> from nipype.testing import example_data
    >>> tca = brainsuite.Tca()
    >>> tca.inputs.inputMaskFile = example_data('mask.nii')
    >>> results = tca.run()

Inputs:

    [Mandatory]
    inputMaskFile: (a file name)
            input mask volume
            flag: -i %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    foregroundDelta: (an integer (int or long), nipype default value: 20)
            foreground delta
            flag: --delta %d
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    maxCorrectionSize: (an integer (int or long))
            minimum correction size
            flag: -n %d
    minCorrectionSize: (an integer (int or long), nipype default value:
             2500)
            maximum correction size
            flag: -m %d
    outputMaskFile: (a file name)
            output mask volume. If unspecified, output file name will be auto
            generated.
            flag: -o %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    timer: (a boolean)
            timing function
            flag: --timer
    verbosity: (an integer (int or long))
            verbosity (0 = quiet)
            flag: -v %d

Outputs:

    outputMaskFile: (a file name)
            path/name of mask file

## ThicknessPVC

Link to code

Wraps command thicknessPVC.sh

ThicknessPVC computes cortical thickness using partial tissue fractions. This thickness measure is then transferred to the atlas surface to facilitate population studies. It also stores the computed thickness into separate hemisphere files and subject thickness mapped to the atlas hemisphere surfaces. ThicknessPVC is not run through the main SVReg sequence, and should be used after executing the BrainSuite and SVReg sequence. For more informaction, please see:

http://brainsuite.org/processing/svreg/svreg_modules/

### Examples

    >>> from nipype.interfaces import brainsuite
    >>> thicknessPVC = brainsuite.ThicknessPVC()
    >>> thicknessPVC.inputs.subjectFilePrefix = 'home/user/btestsubject/testsubject'
    >>> results = thicknessPVC.run()

Inputs:

    [Mandatory]
    subjectFilePrefix: (a unicode string)
            Absolute path and filename prefix of the subject data
            flag: %s

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    None

## `getFileName()`

Link to code

## `l_outputs()`

Link to code