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# nipype.interfaces.brainsuite.brainsuite module

This script provides interfaces for BrainSuite command line tools. Please see brainsuite.org for more information.

Author: Jason Wong

## BDP

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> BVecBValPaira 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 . Maps to a command-line argument: `--bvec %s --bval %s` (position: -1). Mutually exclusive with inputs: `bMatrixFile`.

> bMatrixFilea pathlike object or string representing a file

>   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). . Maps to a command-line argument: `--bmat %s` (position: -1). Mutually exclusive with inputs: `BVecBValPair`.

> bfcFilea pathlike object or string representing a file

>   Specify absolute path to file produced by bfc. By default, bfc produces the file in the format: prefix.bfc.nii.gz. Maps to a command-line argument: `%s` (position: 0). Mutually exclusive with inputs: `noStructuralRegistration`.

> inputDiffusionDataa pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--nii %s` (position: -2).

> noStructuralRegistrationa 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. Maps to a command-line argument: `--no-structural-registration` (position: 0). Mutually exclusive with inputs: `bfcFile`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bValRatioThresholda 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. . Maps to a command-line argument: `--bval-ratio-threshold %f`.

> customDiffusionLabela pathlike object or string representing a file

>   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. Maps to a command-line argument: `--custom-diffusion-label %s`.

> customLabelXMLa pathlike object or string representing a file

>   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. Maps to a command-line argument: `--custom-label-xml %s`.

> customT1Labela pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--custom-t1-label %s`.

> dataSinkDelaya 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/. Maps to a command-line argument: `%s`.

> dcorrRegMeasure‘MI’ or ‘INVERSION-EPI’ or ‘INVERSION-T1’ or ‘INVERSION-BOTH’ or ‘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. . Maps to a command-line argument: `--dcorr-reg-method %s`.

> dcorrWeighta 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. . Maps to a command-line argument: `--dcorr-regularization-wt %f`.

> dwiMaska pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--dwi-mask %s`.

> echoSpacinga float

>   Sets the echo spacing to t seconds, which is used for fieldmap-based distortion correction. This flag is required when using fieldmapCorrection. Maps to a command-line argument: `--echo-spacing=%f`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> estimateODF_3DShorea float

>   Estimates ODFs using 3Dshore. Pass in diffusion time, in ms. Maps to a command-line argument: `--3dshore --diffusion_time_ms %f`.

> estimateODF_FRACTa 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. . Maps to a command-line argument: `--FRACT`.

> estimateODF_FRTa 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. . Maps to a command-line argument: `--FRT`.

> estimateTensorsa 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. . Maps to a command-line argument: `--tensors`.

> fieldmapCorrectiona pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--fieldmap-correction %s`. Requires inputs: `echoSpacing`.

> fieldmapCorrectionMethod‘pixelshift’ or ‘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. Maps to a command-line argument: `--fieldmap-correction-method %s`. Mutually exclusive with inputs: `skipIntensityCorr`.

> fieldmapSmootha 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. . Maps to a command-line argument: `--fieldmap-smooth3=%f`.

> flagConfigFilea pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--flag-conf-file %s`.

> forcePartialROIStatsa 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. . Maps to a command-line argument: `--force-partial-roi-stats`.

> generateStatsa 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. . Maps to a command-line argument: `--generate-stats`.

> ignoreFieldmapFOVa 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. . Maps to a command-line argument: `--ignore-fieldmap-fov`.

> ignoreMemorya 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. . Maps to a command-line argument: `--ignore-memory`.

> lowMemorya 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. . Maps to a command-line argument: `--low-memory`.

> odfLambtaa 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. . Maps to a command-line argument: `--odf-lambda <L>`.

> onlyStatsa 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. . Maps to a command-line argument: `--generate-only-stats`.

> outPrefixa 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. . Maps to a command-line argument: `--output-fileprefix %s`.

> outputDiffusionCoordinatesa 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). . Maps to a command-line argument: `--output-diffusion-coordinate`.

> outputSubdira 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. . Maps to a command-line argument: `--output-subdir %s`.

> phaseEncodingDirection‘x’ or ‘x-’ or ‘y’ or ‘y-’ or ‘z’ or ‘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. . Maps to a command-line argument: `--dir=%s`.

> rigidRegMeasure‘MI’ or ‘INVERSION’ or ‘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. . Maps to a command-line argument: `--rigid-reg-measure %s`.

> skipDistortionCorra 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. . Maps to a command-line argument: `--no-distortion-correction`.

> skipIntensityCorra 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. . Maps to a command-line argument: `--no-intensity-correction`. Mutually exclusive with inputs: `fieldmapCorrectionMethod`.

> skipNonuniformityCorra 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. . Maps to a command-line argument: `--no-nonuniformity-correction`.

> t1Maska pathlike object or string representing a file

>   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). . Maps to a command-line argument: `--t1-mask %s`.

> threadsan 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 . Maps to a command-line argument: `--threads=%d`.

> transformDataOnlya 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. . Maps to a command-line argument: `--transform-data-only`.

> transformDiffusionSurfacea pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--transform-diffusion-surface %s`.

> transformDiffusionVolumea pathlike object or string representing a file

>   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. Maps to a command-line argument: `--transform-diffusion-volume %s`.

> transformInterpolation‘linear’ or ‘nearest’ or ‘cubic’ or ‘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. . Maps to a command-line argument: `--transform-interpolation %s`.

> transformT1Surfacea pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--transform-t1-surface %s`.

> transformT1Volumea pathlike object or string representing a file

>   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. . Maps to a command-line argument: `--transform-t1-volume %s`.

## Bfc

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMRIFilea pathlike object or string representing a file

>   Input skull-stripped MRI volume. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> biasEstimateConvergenceThresholda float

>   Bias estimate convergence threshold (values > 0.1 disable). Maps to a command-line argument: `--beps %f`.

> biasEstimateSpacingan integer (int or long)

>   Bias sample spacing (voxels). Maps to a command-line argument: `-s %d`.

> biasFieldEstimatesOutputPrefixa unicode string

>   Save iterative bias field estimates as <prefix>.n.field.nii.gz. Maps to a command-line argument: `--biasprefix %s`.

> biasRange‘low’ or ‘medium’ or ‘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]

>   Maps to a command-line argument: `%s`.

> controlPointSpacingan integer (int or long)

>   Control point spacing (voxels). Maps to a command-line argument: `-c %d`.

> convergenceThresholda float

>   Convergence threshold. Maps to a command-line argument: `--eps %f`.

> correctWholeVolumea boolean

>   Apply correction field to entire volume. Maps to a command-line argument: `--extrapolate`.

> correctedImagesOutputPrefixa unicode string

>   Save iterative corrected images as <prefix>.n.bfc.nii.gz. Maps to a command-line argument: `--prefix %s`.

> correctionScheduleFilea pathlike object or string representing a file

>   List of parameters . Maps to a command-line argument: `--schedule %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> histogramRadiusan integer (int or long)

>   Histogram radius (voxels). Maps to a command-line argument: `-r %d`.

> histogramType‘ellipse’ or ‘block’

>   Options for type of histogram:

>   > - `ellipse`: use ellipsoid for ROI histogram

>   > - `block`:use block for ROI histogram

>   Maps to a command-line argument: `%s`.

> inputMaskFilea pathlike object or string representing a file

>   Mask file. Maps to a command-line argument: `-m %s`.

> intermediate_file_type‘analyze’ or ‘nifti’ or ‘gzippedAnalyze’ or ‘gzippedNifti’

>   Options for the format in which intermediate files are generated. Maps to a command-line argument: `%s`.

> iterativeModea boolean

>   Iterative mode (overrides -r, -s, -c, -w settings). Maps to a command-line argument: `--iterate`.

> maxBiasa float

>   Maximum allowed bias value. Maps to a command-line argument: `-U %f`. (Nipype default value: `1.5`)

> minBiasa float

>   Minimum allowed bias value. Maps to a command-line argument: `-L %f`. (Nipype default value: `0.5`)

> outputBiasFielda pathlike object or string representing a file

>   Save bias field estimate. Maps to a command-line argument: `--bias %s`.

> outputMRIVolumea pathlike object or string representing a file

>   Output bias-corrected MRI volume. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> outputMaskedBiasFielda pathlike object or string representing a file

>   Save bias field estimate (masked). Maps to a command-line argument: `--maskedbias %s`.

> splineLambdaa float

>   Spline stiffness weighting parameter. Maps to a command-line argument: `-w %f`.

> timera boolean

>   Display timing information. Maps to a command-line argument: `--timer`.

> verbosityLevelan integer (int or long)

>   Verbosity level (0=silent). Maps to a command-line argument: `-v %d`.

> correctionScheduleFilea pathlike object or string representing a file

>   Path/name of schedule file.

> outputBiasFielda pathlike object or string representing a file

>   Path/name of bias field output file.

> outputMRIVolumea pathlike object or string representing a file

>   Path/name of output file.

> outputMaskedBiasFielda pathlike object or string representing a file

>   Path/name of masked bias field output.

## Bse

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMRIFilea pathlike object or string representing a file

>   Input MRI volume. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> diffusionConstanta float

>   Diffusion constant. Maps to a command-line argument: `-d %f`. (Nipype default value: `25`)

> diffusionIterationsan integer (int or long)

>   Diffusion iterations. Maps to a command-line argument: `-n %d`. (Nipype default value: `3`)

> dilateFinalMaska boolean

>   Dilate final mask. Maps to a command-line argument: `-p`. (Nipype default value: `True`)

> edgeDetectionConstanta float

>   Edge detection constant. Maps to a command-line argument: `-s %f`. (Nipype default value: `0.64`)

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> noRotatea boolean

>   Retain original orientation(default behavior will auto-rotate input NII files to LPI orientation). Maps to a command-line argument: `--norotate`.

> outputCortexFilea pathlike object or string representing a file

>   Cortex file. Maps to a command-line argument: `--cortex %s`.

> outputDetailedBrainMaska pathlike object or string representing a file

>   Save detailed brain mask. Maps to a command-line argument: `--hires %s`.

> outputDiffusionFiltera pathlike object or string representing a file

>   Diffusion filter output. Maps to a command-line argument: `--adf %s`.

> outputEdgeMapa pathlike object or string representing a file

>   Edge map output. Maps to a command-line argument: `--edge %s`.

> outputMRIVolumea pathlike object or string representing a file

>   Output brain-masked MRI volume. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> outputMaskFilea pathlike object or string representing a file

>   Save smooth brain mask. If unspecified, output file name will be auto generated. Maps to a command-line argument: `--mask %s`.

> radiusa float

>   Radius of erosion/dilation filter. Maps to a command-line argument: `-r %f`. (Nipype default value: `1`)

> timera boolean

>   Show timing. Maps to a command-line argument: `--timer`.

> trima boolean

>   Trim brainstem. Maps to a command-line argument: `--trim`. (Nipype default value: `True`)

> verbosityLevela float

>   > verbosity level (0=silent).

>   Maps to a command-line argument: `-v %f`. (Nipype default value: `1`)

> outputCortexFilea pathlike object or string representing a file

>   Path/name of cortex file.

> outputDetailedBrainMaska pathlike object or string representing a file

>   Path/name of detailed brain mask.

> outputDiffusionFiltera pathlike object or string representing a file

>   Path/name of diffusion filter output.

> outputEdgeMapa pathlike object or string representing a file

>   Path/name of edge map output.

> outputMRIVolumea pathlike object or string representing a file

>   Path/name of brain-masked MRI volume.

> outputMaskFilea pathlike object or string representing a file

>   Path/name of smooth brain mask.

## Cerebro

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputAtlasLabelFilea pathlike object or string representing a file

>   Atlas labeling. Maps to a command-line argument: `--atlaslabels %s`.

> inputAtlasMRIFilea pathlike object or string representing a file

>   Atlas MRI volume. Maps to a command-line argument: `--atlas %s`.

> inputMRIFilea pathlike object or string representing a file

>   Input 3D MRI volume. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> costFunctionan integer (int or long)

>   0,1,2. Maps to a command-line argument: `-c %d`. (Nipype default value: `2`)

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputBrainMaskFilea pathlike object or string representing a file

>   Brain mask file. Maps to a command-line argument: `-m %s`.

> keepTempFilesa boolean

>   Don’t remove temporary files. Maps to a command-line argument: `--keep`.

> linearConvergencea float

>   Linear convergence. Maps to a command-line argument: `--linconv %f`.

> outputAffineTransformFilea pathlike object or string representing a file

>   Save affine transform to file. Maps to a command-line argument: `--air %s`.

> outputCerebrumMaskFilea pathlike object or string representing a file

>   Output cerebrum mask volume. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> outputLabelVolumeFilea pathlike object or string representing a file

>   Output labeled hemisphere/cerebrum volume. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-l %s`.

> outputWarpTransformFilea pathlike object or string representing a file

>   Save warp transform to file. Maps to a command-line argument: `--warp %s`.

> tempDirectorya unicode string

>   Specify directory to use for temporary files. Maps to a command-line argument: `--tempdir %s`.

> tempDirectoryBasea unicode string

>   Create a temporary directory within this directory. Maps to a command-line argument: `--tempdirbase %s`.

> useCentroidsa boolean

>   Use centroids of data to initialize position. Maps to a command-line argument: `--centroids`.

> verbosityan integer (int or long)

>   Verbosity level (0=silent). Maps to a command-line argument: `-v %d`.

> warpConvergencea float

>   Warp convergence. Maps to a command-line argument: `--warpconv %f`.

> warpLabelan integer (int or long)

>   Warp order (2,3,4,5,6,7,8). Maps to a command-line argument: `--warplevel %d`.

> outputAffineTransformFilea pathlike object or string representing a file

>   Path/name of affine transform file.

> outputCerebrumMaskFilea pathlike object or string representing a file

>   Path/name of cerebrum mask file.

> outputLabelVolumeFilea pathlike object or string representing a file

>   Path/name of label mask file.

> outputWarpTransformFilea pathlike object or string representing a file

>   Path/name of warp transform file.

## Cortex

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputHemisphereLabelFilea pathlike object or string representing a file

>   Hemisphere / lobe label volume. Maps to a command-line argument: `-h %s`.

> inputTissueFractionFilea pathlike object or string representing a file

>   Tissue fraction file (32-bit float). Maps to a command-line argument: `-f %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> computeGCBoundarya boolean

>   Compute GM/CSF boundary. Maps to a command-line argument: `-g`.

> computeWGBoundarya boolean

>   Compute WM/GM boundary. Maps to a command-line argument: `-w`. (Nipype default value: `True`)

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> includeAllSubcorticalAreasa boolean

>   Include all subcortical areas in WM mask. Maps to a command-line argument: `-a`. (Nipype default value: `True`)

> outputCerebrumMaska pathlike object or string representing a file

>   Output structure mask. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> timera boolean

>   Timing function. Maps to a command-line argument: `--timer`.

> tissueFractionThresholda float

>   Tissue fraction threshold (percentage). Maps to a command-line argument: `-p %f`. (Nipype default value: `50.0`)

> verbosityan integer (int or long)

>   Verbosity level. Maps to a command-line argument: `-v %d`.

> outputCerebrumMaska pathlike object or string representing a file

>   Path/name of cerebrum mask.

## Dewisp

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMaskFilea pathlike object or string representing a file

>   Input file. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> maximumIterationsan integer (int or long)

>   Maximum number of iterations. Maps to a command-line argument: `-n %d`.

> outputMaskFilea pathlike object or string representing a file

>   Output file. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> sizeThresholdan integer (int or long)

>   Size threshold. Maps to a command-line argument: `-t %d`.

> timera boolean

>   Time processing. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity. Maps to a command-line argument: `-v %d`.

> outputMaskFilea pathlike object or string representing a file

>   Path/name of mask file.

## Dfs

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputVolumeFilea pathlike object or string representing a file

>   Input 3D volume. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curvatureWeightinga float

>   Curvature weighting. Maps to a command-line argument: `-w %f`. (Nipype default value: `5.0`)

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputShadingVolumea pathlike object or string representing a file

>   Shade surface model with data from image volume. Maps to a command-line argument: `-c %s`.

> noNormalsFlaga boolean

>   Do not compute vertex normals. Maps to a command-line argument: `--nonormals`.

> nonZeroTessellationa boolean

>   Tessellate non-zero voxels. Maps to a command-line argument: `-nz`. Mutually exclusive with inputs: `nonZeroTessellation`, `specialTessellation`.

> outputSurfaceFilea pathlike object or string representing a file

>   Output surface mesh file. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> postSmoothFlaga boolean

>   Smooth vertices after coloring. Maps to a command-line argument: `--postsmooth`.

> scalingPercentilea float

>   Scaling percentile. Maps to a command-line argument: `-f %f`.

> smoothingConstanta float

>   Smoothing constant. Maps to a command-line argument: `-a %f`. (Nipype default value: `0.5`)

> smoothingIterationsan integer (int or long)

>   Number of smoothing iterations. Maps to a command-line argument: `-n %d`. (Nipype default value: `10`)

> specialTessellation‘greater_than’ or ‘less_than’ or ‘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>. Maps to a command-line argument: `%s` (position: -1). Mutually exclusive with inputs: `nonZeroTessellation`, `specialTessellation`. Requires inputs: `tessellationThreshold`.

> tessellationThresholda 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>. Maps to a command-line argument: `%f`.

> timera boolean

>   Timing function. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity (0 = quiet). Maps to a command-line argument: `-v %d`.

> zeroPadFlaga boolean

>   Zero-pad volume (avoids clipping at edges). Maps to a command-line argument: `-z`.

> outputSurfaceFilea pathlike object or string representing a file

>   Path/name of surface file.

## Hemisplit

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputHemisphereLabelFilea pathlike object or string representing a file

>   Input hemisphere label volume. Maps to a command-line argument: `-l %s`.

> inputSurfaceFilea pathlike object or string representing a file

>   Input surface. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> outputLeftHemispherea pathlike object or string representing a file

>   Output surface file, left hemisphere. If unspecified, output file name will be auto generated. Maps to a command-line argument: `--left %s`.

> outputLeftPialHemispherea pathlike object or string representing a file

>   Output pial surface file, left hemisphere. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-pl %s`.

> outputRightHemispherea pathlike object or string representing a file

>   Output surface file, right hemisphere. If unspecified, output file name will be auto generated. Maps to a command-line argument: `--right %s`.

> outputRightPialHemispherea pathlike object or string representing a file

>   Output pial surface file, right hemisphere. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-pr %s`.

> pialSurfaceFilea pathlike object or string representing a file

>   Pial surface file – must have same geometry as input surface. Maps to a command-line argument: `-p %s`.

> timera boolean

>   Timing function. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity (0 = silent). Maps to a command-line argument: `-v %d`.

> outputLeftHemispherea pathlike object or string representing a file

>   Path/name of left hemisphere.

> outputLeftPialHemispherea pathlike object or string representing a file

>   Path/name of left pial hemisphere.

> outputRightHemispherea pathlike object or string representing a file

>   Path/name of right hemisphere.

> outputRightPialHemispherea pathlike object or string representing a file

>   Path/name of right pial hemisphere.

## Pialmesh

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMaskFilea pathlike object or string representing a file

>   Restrict growth to mask file region. Maps to a command-line argument: `-m %s`.

> inputSurfaceFilea pathlike object or string representing a file

>   Input file. Maps to a command-line argument: `-i %s`.

> inputTissueFractionFilea pathlike object or string representing a file

>   Floating point (32) tissue fraction image. Maps to a command-line argument: `-f %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> exportPrefixa unicode string

>   Prefix for exporting surfaces if interval is set. Maps to a command-line argument: `--prefix %s`.

> laplacianSmoothinga float

>   Apply Laplacian smoothing. Maps to a command-line argument: `--smooth %f`. (Nipype default value: `0.025`)

> maxThicknessa float

>   Maximum allowed tissue thickness. Maps to a command-line argument: `--max %f`. (Nipype default value: `20`)

> normalSmoothera float

>   Strength of normal smoother. Maps to a command-line argument: `--nc %f`. (Nipype default value: `0.2`)

> numIterationsan integer (int or long)

>   Number of iterations. Maps to a command-line argument: `-n %d`. (Nipype default value: `100`)

> outputIntervalan integer (int or long)

>   Output interval. Maps to a command-line argument: `--interval %d`. (Nipype default value: `10`)

> outputSurfaceFilea pathlike object or string representing a file

>   Output file. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> recomputeNormalsa boolean

>   Recompute normals at each iteration. Maps to a command-line argument: `--norm`.

> searchRadiusa float

>   Search radius. Maps to a command-line argument: `-r %f`. (Nipype default value: `1`)

> stepSizea float

>   Step size. Maps to a command-line argument: `-s %f`. (Nipype default value: `0.4`)

> tangentSmoothera float

>   Strength of tangential smoother. Maps to a command-line argument: `--tc %f`.

> timera boolean

>   Show timing. Maps to a command-line argument: `--timer`.

> tissueThresholda float

>   Tissue threshold. Maps to a command-line argument: `-t %f`. (Nipype default value: `1.05`)

> verbosityan integer (int or long)

>   Verbosity. Maps to a command-line argument: `-v %d`.

> outputSurfaceFilea pathlike object or string representing a file

>   Path/name of surface file.

## Pvc

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMRIFilea pathlike object or string representing a file

>   MRI file. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputMaskFilea pathlike object or string representing a file

>   Brain mask file. Maps to a command-line argument: `-m %s`.

> outputLabelFilea pathlike object or string representing a file

>   Output label file. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> outputTissueFractionFilea pathlike object or string representing a file

>   Output tissue fraction file. Maps to a command-line argument: `-f %s`.

> spatialPriora float

>   Spatial prior strength. Maps to a command-line argument: `-l %f`.

> threeClassFlaga boolean

>   Use a three-class (CSF=0,GM=1,WM=2) labeling. Maps to a command-line argument: `-3`.

> timera boolean

>   Time processing. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity level (0 = silent). Maps to a command-line argument: `-v %d`.

> outputLabelFilea pathlike object or string representing a file

>   Path/name of label file.

> outputTissueFractionFilea pathlike object or string representing a file

>   Path/name of tissue fraction file.

## SVReg

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> subjectFilePrefixa unicode string

>   Absolute path and filename prefix of the subjects output from BrainSuite Cortical Surface Extraction Sequence. Maps to a command-line argument: `'%s'` (position: 0).

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> atlasFilePrefixa 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. Maps to a command-line argument: `'%s'` (position: 1).

> curveMatchingInstructionsa 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”. Maps to a command-line argument: `'-cur %s'`.

> dataSinkDelaya 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 . Maps to a command-line argument: `%s`.

> displayModuleNamea boolean

>   Module name will be displayed in the messages. Maps to a command-line argument: `'-m'`.

> displayTimestampsa boolean

>   Timestamps will be displayed in the messages. Maps to a command-line argument: `'-t'`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> iterationsan integer (int or long)

>   Assigns a number of iterations in the intensity registration step.if unspecified, performs 100 iterations. Maps to a command-line argument: `'-H %d'`.

> keepIntermediatesa boolean

>   Keep the intermediate files after the svreg sequence is complete. Maps to a command-line argument: `'-k'`.

> pialSurfaceMaskDilationan 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). Maps to a command-line argument: `'-D %d'`.

> refineOutputsa boolean

>   Refine outputs at the expense of more processing time. Maps to a command-line argument: `'-r'`.

> shortMessagesa boolean

>   Short messages instead of detailed messages. Maps to a command-line argument: `'-gui'`.

> skipToIntensityRega 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. Maps to a command-line argument: `'-p'`.

> skipToVolumeRega 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. Maps to a command-line argument: `'-s'`.

> skipVolumetricProcessinga boolean

>   Only surface registration and labeling will be performed. Volumetric processing will be skipped. Maps to a command-line argument: `'-S'`.

> useCerebrumMaska 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. Maps to a command-line argument: `'-C'`.

> useManualMaskFilea 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. Maps to a command-line argument: `'-cbm'`.

> useMultiThreadinga boolean

>   If multiple CPUs are present on the system, the code will try to use multithreading to make the execution fast. Maps to a command-line argument: `'-P'`.

> useSingleThreadinga boolean

>   Use single threaded mode. Maps to a command-line argument: `'-U'`.

> verbosity0a boolean

>   No messages will be reported. Maps to a command-line argument: `'-v0'`. Mutually exclusive with inputs: `verbosity0`, `verbosity1`, `verbosity2`.

> verbosity1a boolean

>   Messages will be reported but not the iteration-wise detailed messages. Maps to a command-line argument: `'-v1'`. Mutually exclusive with inputs: `verbosity0`, `verbosity1`, `verbosity2`.

> verbosity2a boolean

>   All the messages, including per-iteration, will be displayed. Maps to a command-line argument: `'v2'`. Mutually exclusive with inputs: `verbosity0`, `verbosity1`, `verbosity2`.

## Scrubmask

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMaskFilea pathlike object or string representing a file

>   Input structure mask file. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> backgroundFillThresholdan integer (int or long)

>   Background fill threshold. Maps to a command-line argument: `-b %d`. (Nipype default value: `2`)

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> foregroundTrimThresholdan integer (int or long)

>   Foreground trim threshold. Maps to a command-line argument: `-f %d`. (Nipype default value: `0`)

> numberIterationsan integer (int or long)

>   Number of iterations. Maps to a command-line argument: `-n %d`.

> outputMaskFilea pathlike object or string representing a file

>   Output structure mask file. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> timera boolean

>   Timing function. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity (0=silent). Maps to a command-line argument: `-v %d`.

> outputMaskFilea pathlike object or string representing a file

>   Path/name of mask file.

## Skullfinder

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMRIFilea pathlike object or string representing a file

>   Input file. Maps to a command-line argument: `-i %s`.

> inputMaskFilea pathlike object or string representing a file

>   A brain mask file, 8-bit image (0=non-brain, 255=brain). Maps to a command-line argument: `-m %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bgLabelValuean integer (int or long)

>   Background label value (0-255). Maps to a command-line argument: `--bglabel %d`.

> brainLabelValuean integer (int or long)

>   Brain label value (0-255). Maps to a command-line argument: `--brainlabel %d`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> lowerThresholdan integer (int or long)

>   Lower threshold for segmentation. Maps to a command-line argument: `-l %d`.

> outputLabelFilea pathlike object or string representing a file

>   Output multi-colored label volume segmenting brain, scalp, inner skull & outer skull If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> performFinalOpeninga boolean

>   Perform a final opening operation on the scalp mask. Maps to a command-line argument: `--finalOpening`.

> scalpLabelValuean integer (int or long)

>   Scalp label value (0-255). Maps to a command-line argument: `--scalplabel %d`.

> skullLabelValuean integer (int or long)

>   Skull label value (0-255). Maps to a command-line argument: `--skulllabel %d`.

> spaceLabelValuean integer (int or long)

>   Space label value (0-255). Maps to a command-line argument: `--spacelabel %d`.

> surfaceFilePrefixa unicode string

>   If specified, generate surface files for brain, skull, and scalp. Maps to a command-line argument: `-s %s`.

> upperThresholdan integer (int or long)

>   Upper threshold for segmentation. Maps to a command-line argument: `-u %d`.

> verbosityan integer (int or long)

>   Verbosity. Maps to a command-line argument: `-v %d`.

> outputLabelFilea pathlike object or string representing a file

>   Path/name of label file.

## Tca

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> inputMaskFilea pathlike object or string representing a file

>   Input mask volume. Maps to a command-line argument: `-i %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> foregroundDeltaan integer (int or long)

>   Foreground delta. Maps to a command-line argument: `--delta %d`. (Nipype default value: `20`)

> maxCorrectionSizean integer (int or long)

>   Minimum correction size. Maps to a command-line argument: `-n %d`.

> minCorrectionSizean integer (int or long)

>   Maximum correction size. Maps to a command-line argument: `-m %d`. (Nipype default value: `2500`)

> outputMaskFilea pathlike object or string representing a file

>   Output mask volume. If unspecified, output file name will be auto generated. Maps to a command-line argument: `-o %s`.

> timera boolean

>   Timing function. Maps to a command-line argument: `--timer`.

> verbosityan integer (int or long)

>   Verbosity (0 = quiet). Maps to a command-line argument: `-v %d`.

> outputMaskFilea pathlike object or string representing a file

>   Path/name of mask file.

## ThicknessPVC

Link to code

> Bases: CommandLine

> Wrapped executable: `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()

> subjectFilePrefixa unicode string

>   Absolute path and filename prefix of the subject data. Maps to a command-line argument: `%s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

nipype.interfaces.brainsuite.brainsuite.getFileName(inputName, suffix)

nipype.interfaces.brainsuite.brainsuite.l_outputs(self)