interfaces.brainsuite.brainsuite¶
BDP¶
Wraps the executable 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]
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
argument: ``%s``, position: 0
mutually_exclusive: noStructuralRegistration
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.
argument: ``--no-structural-registration``, position: 0
mutually_exclusive: bfcFile
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.
argument: ``--nii %s``, position: -2
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).
argument: ``--bmat %s``, position: -1
mutually_exclusive: BVecBValPair
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
argument: ``--bvec %s --bval %s``, position: -1
mutually_exclusive: bMatrixFile
[Optional]
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/
argument: ``%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.
argument: ``--dir=%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
argument: ``--echo-spacing=%f``
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.
argument: ``--bval-ratio-threshold %f``
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.
argument: ``--tensors``
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.
argument: ``--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.
argument: ``--FRT``
estimateODF_3DShore: (a float)
Estimates ODFs using 3Dshore. Pass in diffusion time, in ms
argument: ``--3dshore --diffusion_time_ms %f``
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.
argument: ``--odf-lambda <L>``
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).
argument: ``--t1-mask %s``
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.
argument: ``--dwi-mask %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.
argument: ``--rigid-reg-measure %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.
argument: ``--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.
argument: ``--dcorr-regularization-wt %f``
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.
argument: ``--no-distortion-correction``
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.
argument: ``--no-nonuniformity-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.
argument: ``--no-intensity-correction``
mutually_exclusive: fieldmapCorrectionMethod
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.
argument: ``--fieldmap-correction %s``
requires: 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.
argument: ``--fieldmap-correction-method %s``
mutually_exclusive: skipIntensityCorr
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.
argument: ``--ignore-fieldmap-fov``
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.
argument: ``--fieldmap-smooth3=%f``
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
argument: ``--transform-diffusion-volume %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.
argument: ``--transform-t1-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.
argument: ``--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.
argument: ``--transform-t1-surface %s``
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.
argument: ``--transform-diffusion-surface %s``
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.
argument: ``--transform-data-only``
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.
argument: ``--generate-stats``
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.
argument: ``--generate-only-stats``
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.
argument: ``--force-partial-roi-stats``
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
argument: ``--custom-diffusion-label %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.
argument: ``--custom-t1-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.
argument: ``--custom-label-xml %s``
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.
argument: ``--output-subdir %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).
argument: ``--output-diffusion-coordinate``
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.
argument: ``--flag-conf-file %s``
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.
argument: ``--output-fileprefix %s``
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
argument: ``--threads=%d``
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.
argument: ``--low-memory``
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.
argument: ``--ignore-memory``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
None
Bfc¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
inputMaskFile: (a file name)
mask file
argument: ``-m %s``
outputMRIVolume: (a file name)
output bias-corrected MRI volume.If unspecified, output file name
will be auto generated.
argument: ``-o %s``
outputBiasField: (a file name)
save bias field estimate
argument: ``--bias %s``
outputMaskedBiasField: (a file name)
save bias field estimate (masked)
argument: ``--maskedbias %s``
histogramRadius: (an integer (int or long))
histogram radius (voxels)
argument: ``-r %d``
biasEstimateSpacing: (an integer (int or long))
bias sample spacing (voxels)
argument: ``-s %d``
controlPointSpacing: (an integer (int or long))
control point spacing (voxels)
argument: ``-c %d``
splineLambda: (a float)
spline stiffness weighting parameter
argument: ``-w %f``
histogramType: ('ellipse' or 'block')
Options for type of histogram
ellipse: use ellipsoid for ROI histogram
block :use block for ROI histogram
argument: ``%s``
iterativeMode: (a boolean)
iterative mode (overrides -r, -s, -c, -w settings)
argument: ``--iterate``
correctionScheduleFile: (a file name)
list of parameters
argument: ``--schedule %s``
biasFieldEstimatesOutputPrefix: (a unicode string)
save iterative bias field estimates as <prefix>.n.field.nii.gz
argument: ``--biasprefix %s``
correctedImagesOutputPrefix: (a unicode string)
save iterative corrected images as <prefix>.n.bfc.nii.gz
argument: ``--prefix %s``
correctWholeVolume: (a boolean)
apply correction field to entire volume
argument: ``--extrapolate``
minBias: (a float, nipype default value: 0.5)
minimum allowed bias value
argument: ``-L %f``
maxBias: (a float, nipype default value: 1.5)
maximum allowed bias value
argument: ``-U %f``
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]
argument: ``%s``
intermediate_file_type: ('analyze' or 'nifti' or 'gzippedAnalyze' or
'gzippedNifti')
Options for the format in which intermediate files are generated
argument: ``%s``
convergenceThreshold: (a float)
convergence threshold
argument: ``--eps %f``
biasEstimateConvergenceThreshold: (a float)
bias estimate convergence threshold (values > 0.1 disable)
argument: ``--beps %f``
verbosityLevel: (an integer (int or long))
verbosity level (0=silent)
argument: ``-v %d``
timer: (a boolean)
display timing information
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputMRIVolume: (a file name)
path/name of output file
outputBiasField: (a file name)
path/name of bias field output file
outputMaskedBiasField: (a file name)
path/name of masked bias field output
correctionScheduleFile: (a file name)
path/name of schedule file
Bse¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
outputMRIVolume: (a file name)
output brain-masked MRI volume. If unspecified, output file name
will be auto generated.
argument: ``-o %s``
outputMaskFile: (a file name)
save smooth brain mask. If unspecified, output file name will be
auto generated.
argument: ``--mask %s``
diffusionConstant: (a float, nipype default value: 25)
diffusion constant
argument: ``-d %f``
diffusionIterations: (an integer (int or long), nipype default value:
3)
diffusion iterations
argument: ``-n %d``
edgeDetectionConstant: (a float, nipype default value: 0.64)
edge detection constant
argument: ``-s %f``
radius: (a float, nipype default value: 1)
radius of erosion/dilation filter
argument: ``-r %f``
dilateFinalMask: (a boolean, nipype default value: True)
dilate final mask
argument: ``-p``
trim: (a boolean, nipype default value: True)
trim brainstem
argument: ``--trim``
outputDiffusionFilter: (a file name)
diffusion filter output
argument: ``--adf %s``
outputEdgeMap: (a file name)
edge map output
argument: ``--edge %s``
outputDetailedBrainMask: (a file name)
save detailed brain mask
argument: ``--hires %s``
outputCortexFile: (a file name)
cortex file
argument: ``--cortex %s``
verbosityLevel: (a float, nipype default value: 1)
verbosity level (0=silent)
argument: ``-v %f``
noRotate: (a boolean)
retain original orientation(default behavior will auto-rotate input
NII files to LPI orientation)
argument: ``--norotate``
timer: (a boolean)
show timing
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputMRIVolume: (a file name)
path/name of brain-masked MRI volume
outputMaskFile: (a file name)
path/name of smooth brain mask
outputDiffusionFilter: (a file name)
path/name of diffusion filter output
outputEdgeMap: (a file name)
path/name of edge map output
outputDetailedBrainMask: (a file name)
path/name of detailed brain mask
outputCortexFile: (a file name)
path/name of cortex file
Cerebro¶
Wraps the executable 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]
inputMRIFile: (a file name)
input 3D MRI volume
argument: ``-i %s``
inputAtlasMRIFile: (a file name)
atlas MRI volume
argument: ``--atlas %s``
inputAtlasLabelFile: (a file name)
atlas labeling
argument: ``--atlaslabels %s``
[Optional]
inputBrainMaskFile: (a file name)
brain mask file
argument: ``-m %s``
outputCerebrumMaskFile: (a file name)
output cerebrum mask volume. If unspecified, output file name will
be auto generated.
argument: ``-o %s``
outputLabelVolumeFile: (a file name)
output labeled hemisphere/cerebrum volume. If unspecified, output
file name will be auto generated.
argument: ``-l %s``
costFunction: (an integer (int or long), nipype default value: 2)
0,1,2
argument: ``-c %d``
useCentroids: (a boolean)
use centroids of data to initialize position
argument: ``--centroids``
outputAffineTransformFile: (a file name)
save affine transform to file.
argument: ``--air %s``
outputWarpTransformFile: (a file name)
save warp transform to file.
argument: ``--warp %s``
verbosity: (an integer (int or long))
verbosity level (0=silent)
argument: ``-v %d``
linearConvergence: (a float)
linear convergence
argument: ``--linconv %f``
warpLabel: (an integer (int or long))
warp order (2,3,4,5,6,7,8)
argument: ``--warplevel %d``
warpConvergence: (a float)
warp convergence
argument: ``--warpconv %f``
keepTempFiles: (a boolean)
don't remove temporary files
argument: ``--keep``
tempDirectory: (a unicode string)
specify directory to use for temporary files
argument: ``--tempdir %s``
tempDirectoryBase: (a unicode string)
create a temporary directory within this directory
argument: ``--tempdirbase %s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputCerebrumMaskFile: (a file name)
path/name of cerebrum mask file
outputLabelVolumeFile: (a file name)
path/name of label mask file
outputAffineTransformFile: (a file name)
path/name of affine transform file
outputWarpTransformFile: (a file name)
path/name of warp transform file
Cortex¶
Wraps the executable 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
argument: ``-h %s``
inputTissueFractionFile: (a file name)
tissue fraction file (32-bit float)
argument: ``-f %s``
[Optional]
outputCerebrumMask: (a file name)
output structure mask. If unspecified, output file name will be auto
generated.
argument: ``-o %s``
tissueFractionThreshold: (a float, nipype default value: 50.0)
tissue fraction threshold (percentage)
argument: ``-p %f``
computeWGBoundary: (a boolean, nipype default value: True)
compute WM/GM boundary
argument: ``-w``
computeGCBoundary: (a boolean)
compute GM/CSF boundary
argument: ``-g``
includeAllSubcorticalAreas: (a boolean, nipype default value: True)
include all subcortical areas in WM mask
argument: ``-a``
verbosity: (an integer (int or long))
verbosity level
argument: ``-v %d``
timer: (a boolean)
timing function
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputCerebrumMask: (a file name)
path/name of cerebrum mask
Dewisp¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
outputMaskFile: (a file name)
output file. If unspecified, output file name will be auto
generated.
argument: ``-o %s``
verbosity: (an integer (int or long))
verbosity
argument: ``-v %d``
sizeThreshold: (an integer (int or long))
size threshold
argument: ``-t %d``
maximumIterations: (an integer (int or long))
maximum number of iterations
argument: ``-n %d``
timer: (a boolean)
time processing
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputMaskFile: (a file name)
path/name of mask file
Dfs¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
outputSurfaceFile: (a file name)
output surface mesh file. If unspecified, output file name will be
auto generated.
argument: ``-o %s``
inputShadingVolume: (a file name)
shade surface model with data from image volume
argument: ``-c %s``
smoothingIterations: (an integer (int or long), nipype default value:
10)
number of smoothing iterations
argument: ``-n %d``
smoothingConstant: (a float, nipype default value: 0.5)
smoothing constant
argument: ``-a %f``
curvatureWeighting: (a float, nipype default value: 5.0)
curvature weighting
argument: ``-w %f``
scalingPercentile: (a float)
scaling percentile
argument: ``-f %f``
nonZeroTessellation: (a boolean)
tessellate non-zero voxels
argument: ``-nz``
mutually_exclusive: nonZeroTessellation, specialTessellation
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>
argument: ``%f``
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>
argument: ``%s``, position: -1
mutually_exclusive: nonZeroTessellation, specialTessellation
requires: tessellationThreshold
zeroPadFlag: (a boolean)
zero-pad volume (avoids clipping at edges)
argument: ``-z``
noNormalsFlag: (a boolean)
do not compute vertex normals
argument: ``--nonormals``
postSmoothFlag: (a boolean)
smooth vertices after coloring
argument: ``--postsmooth``
verbosity: (an integer (int or long))
verbosity (0 = quiet)
argument: ``-v %d``
timer: (a boolean)
timing function
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputSurfaceFile: (a file name)
path/name of surface file
Hemisplit¶
Wraps the executable 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]
inputSurfaceFile: (a file name)
input surface
argument: ``-i %s``
inputHemisphereLabelFile: (a file name)
input hemisphere label volume
argument: ``-l %s``
[Optional]
outputLeftHemisphere: (a file name)
output surface file, left hemisphere. If unspecified, output file
name will be auto generated.
argument: ``--left %s``
outputRightHemisphere: (a file name)
output surface file, right hemisphere. If unspecified, output file
name will be auto generated.
argument: ``--right %s``
pialSurfaceFile: (a file name)
pial surface file -- must have same geometry as input surface
argument: ``-p %s``
outputLeftPialHemisphere: (a file name)
output pial surface file, left hemisphere. If unspecified, output
file name will be auto generated.
argument: ``-pl %s``
outputRightPialHemisphere: (a file name)
output pial surface file, right hemisphere. If unspecified, output
file name will be auto generated.
argument: ``-pr %s``
verbosity: (an integer (int or long))
verbosity (0 = silent)
argument: ``-v %d``
timer: (a boolean)
timing function
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputLeftHemisphere: (a file name)
path/name of left hemisphere
outputRightHemisphere: (a file name)
path/name of right hemisphere
outputLeftPialHemisphere: (a file name)
path/name of left pial hemisphere
outputRightPialHemisphere: (a file name)
path/name of right pial hemisphere
Pialmesh¶
Wraps the executable 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]
inputSurfaceFile: (a file name)
input file
argument: ``-i %s``
inputTissueFractionFile: (a file name)
floating point (32) tissue fraction image
argument: ``-f %s``
inputMaskFile: (a file name)
restrict growth to mask file region
argument: ``-m %s``
[Optional]
outputSurfaceFile: (a file name)
output file. If unspecified, output file name will be auto
generated.
argument: ``-o %s``
verbosity: (an integer (int or long))
verbosity
argument: ``-v %d``
numIterations: (an integer (int or long), nipype default value: 100)
number of iterations
argument: ``-n %d``
searchRadius: (a float, nipype default value: 1)
search radius
argument: ``-r %f``
stepSize: (a float, nipype default value: 0.4)
step size
argument: ``-s %f``
maxThickness: (a float, nipype default value: 20)
maximum allowed tissue thickness
argument: ``--max %f``
tissueThreshold: (a float, nipype default value: 1.05)
tissue threshold
argument: ``-t %f``
outputInterval: (an integer (int or long), nipype default value: 10)
output interval
argument: ``--interval %d``
exportPrefix: (a unicode string)
prefix for exporting surfaces if interval is set
argument: ``--prefix %s``
laplacianSmoothing: (a float, nipype default value: 0.025)
apply Laplacian smoothing
argument: ``--smooth %f``
timer: (a boolean)
show timing
argument: ``--timer``
recomputeNormals: (a boolean)
recompute normals at each iteration
argument: ``--norm``
normalSmoother: (a float, nipype default value: 0.2)
strength of normal smoother.
argument: ``--nc %f``
tangentSmoother: (a float)
strength of tangential smoother.
argument: ``--tc %f``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputSurfaceFile: (a file name)
path/name of surface file
Pvc¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
inputMaskFile: (a file name)
brain mask file
argument: ``-m %s``
outputLabelFile: (a file name)
output label file. If unspecified, output file name will be auto
generated.
argument: ``-o %s``
outputTissueFractionFile: (a file name)
output tissue fraction file
argument: ``-f %s``
spatialPrior: (a float)
spatial prior strength
argument: ``-l %f``
verbosity: (an integer (int or long))
verbosity level (0 = silent)
argument: ``-v %d``
threeClassFlag: (a boolean)
use a three-class (CSF=0,GM=1,WM=2) labeling
argument: ``-3``
timer: (a boolean)
time processing
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputLabelFile: (a file name)
path/name of label file
outputTissueFractionFile: (a file name)
path/name of tissue fraction file
SVReg¶
Wraps the executable 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
argument: ``'%s'``, position: 0
[Optional]
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
argument: ``%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
argument: ``'%s'``, position: 1
iterations: (an integer (int or long))
Assigns a number of iterations in the intensity registration step.if
unspecified, performs 100 iterations
argument: ``'-H %d'``
refineOutputs: (a boolean)
Refine outputs at the expense of more processing time.
argument: ``'-r'``
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.
argument: ``'-s'``
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.
argument: ``'-p'``
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.
argument: ``'-cbm'``
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"
argument: ``'-cur %s'``
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.
argument: ``'-C'``
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)
argument: ``'-D %d'``
keepIntermediates: (a boolean)
Keep the intermediate files after the svreg sequence is complete.
argument: ``'-k'``
verbosity0: (a boolean)
no messages will be reported
argument: ``'-v0'``
mutually_exclusive: verbosity0, verbosity1, verbosity2
verbosity1: (a boolean)
messages will be reported but not the iteration-wise detailed
messages
argument: ``'-v1'``
mutually_exclusive: verbosity0, verbosity1, verbosity2
verbosity2: (a boolean)
all the messages, including per-iteration, will be displayed
argument: ``'v2'``
mutually_exclusive: verbosity0, verbosity1, verbosity2
shortMessages: (a boolean)
Short messages instead of detailed messages
argument: ``'-gui'``
displayModuleName: (a boolean)
Module name will be displayed in the messages
argument: ``'-m'``
displayTimestamps: (a boolean)
Timestamps will be displayed in the messages
argument: ``'-t'``
skipVolumetricProcessing: (a boolean)
Only surface registration and labeling will be performed. Volumetric
processing will be skipped.
argument: ``'-S'``
useMultiThreading: (a boolean)
If multiple CPUs are present on the system, the code will try to use
multithreading to make the execution fast.
argument: ``'-P'``
useSingleThreading: (a boolean)
Use single threaded mode.
argument: ``'-U'``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
None
Scrubmask¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
outputMaskFile: (a file name)
output structure mask file. If unspecified, output file name will be
auto generated.
argument: ``-o %s``
backgroundFillThreshold: (an integer (int or long), nipype default
value: 2)
background fill threshold
argument: ``-b %d``
foregroundTrimThreshold: (an integer (int or long), nipype default
value: 0)
foreground trim threshold
argument: ``-f %d``
numberIterations: (an integer (int or long))
number of iterations
argument: ``-n %d``
verbosity: (an integer (int or long))
verbosity (0=silent)
argument: ``-v %d``
timer: (a boolean)
timing function
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputMaskFile: (a file name)
path/name of mask file
Skullfinder¶
Wraps the executable 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
argument: ``-i %s``
inputMaskFile: (a file name)
A brain mask file, 8-bit image (0=non-brain, 255=brain)
argument: ``-m %s``
[Optional]
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.
argument: ``-o %s``
verbosity: (an integer (int or long))
verbosity
argument: ``-v %d``
lowerThreshold: (an integer (int or long))
Lower threshold for segmentation
argument: ``-l %d``
upperThreshold: (an integer (int or long))
Upper threshold for segmentation
argument: ``-u %d``
surfaceFilePrefix: (a unicode string)
if specified, generate surface files for brain, skull, and scalp
argument: ``-s %s``
bgLabelValue: (an integer (int or long))
background label value (0-255)
argument: ``--bglabel %d``
scalpLabelValue: (an integer (int or long))
scalp label value (0-255)
argument: ``--scalplabel %d``
skullLabelValue: (an integer (int or long))
skull label value (0-255)
argument: ``--skulllabel %d``
spaceLabelValue: (an integer (int or long))
space label value (0-255)
argument: ``--spacelabel %d``
brainLabelValue: (an integer (int or long))
brain label value (0-255)
argument: ``--brainlabel %d``
performFinalOpening: (a boolean)
perform a final opening operation on the scalp mask
argument: ``--finalOpening``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputLabelFile: (a file name)
path/name of label file
Tca¶
Wraps the executable 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
argument: ``-i %s``
[Optional]
outputMaskFile: (a file name)
output mask volume. If unspecified, output file name will be auto
generated.
argument: ``-o %s``
minCorrectionSize: (an integer (int or long), nipype default value:
2500)
maximum correction size
argument: ``-m %d``
maxCorrectionSize: (an integer (int or long))
minimum correction size
argument: ``-n %d``
foregroundDelta: (an integer (int or long), nipype default value: 20)
foreground delta
argument: ``--delta %d``
verbosity: (an integer (int or long))
verbosity (0 = quiet)
argument: ``-v %d``
timer: (a boolean)
timing function
argument: ``--timer``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
outputMaskFile: (a file name)
path/name of mask file
ThicknessPVC¶
Wraps the executable 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
argument: ``%s``
[Optional]
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a 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', nipype default value: {})
Environment variables
Outputs:
None