class PETStandardUptakeValueComputationInputSpec(CommandLineInputSpec):
petDICOMPath = Directory(desc="Input path to a directory containing a PET volume containing DICOM header information for SUV computation", exists=True, argstr="--petDICOMPath %s")
petVolume = File(desc="Input PET volume for SUVbw computation (must be the same volume as pointed to by the DICOM path!).", exists=True, argstr="--petVolume %s")
labelMap = File(desc="Input label volume containing the volumes of interest", exists=True, argstr="--labelMap %s")
color = File(desc="Color table to to map labels to colors and names", exists=True, argstr="--color %s")
csvFile = traits.Either(traits.Bool, File(), hash_files=False, desc="A file holding the output SUV values in comma separated lines, one per label. Optional.", argstr="--csvFile %s")
OutputLabel = traits.Str(desc="List of labels for which SUV values were computed", argstr="--OutputLabel %s")
OutputLabelValue = traits.Str(desc="List of label values for which SUV values were computed", argstr="--OutputLabelValue %s")
SUVMax = traits.Str(desc="SUV max for each label", argstr="--SUVMax %s")
SUVMean = traits.Str(desc="SUV mean for each label", argstr="--SUVMean %s")
SUVMin = traits.Str(desc="SUV minimum for each label", argstr="--SUVMin %s")
class SubjectSummaryInputSpec(BaseInterfaceInputSpec):
t1w = InputMultiObject(File(exists=True), desc='T1w structural images')
t2w = InputMultiObject(File(exists=True), desc='T2w structural images')
subjects_dir = Directory(desc='FreeSurfer subjects directory')
subject_id = Str(desc='Subject ID')
bold = InputMultiObject(traits.Either(
File(exists=True), traits.List(File(exists=True))),
desc='BOLD functional series')
other_ts = InputMultiObject(traits.Either(
File(exists=True), traits.List(File(exists=True))),
desc='Other series')
class CopyDirInputSpec(CommandLineInputSpec):
src = File(mandatory=True, desc='source file', argstr='%s', position=0)
base_dir = Directory(mandatory=True,
desc='root directory',
argstr='%s',
position=1)
dst = File(genfile=True,
argstr='%s',
position=2,
desc=("The destination file"))
method = traits.Int(mandatory=True, desc='method', argstr='%s', position=3)
class ToPolarCoordsOutputSpec(TraitedSpec):
combined_images = traits.List(
File(exists=True),
desc="List of combined images for each echo using least squares")
first_echo = File(exists=True,
desc="The first echo of the combined images")
last_echo = File(exists=True,
desc="The last echo of the combined images")
coil_magnitudes = traits.List(
traits.List(File(exists=True)),
desc=("List of magnitude images for each coil for each echo"))
coil_phases = traits.List(
traits.List(File(exists=True)),
desc=("List of magnitude images for each coil for each echo"))
combined_dir = Directory(exists=True, desc=(
"Output directory for combined magnitude images for each echo time "))
magnitudes_dir = Directory(exists=True, desc=(
"Output directory for coil magnitude images"))
phases_dir = Directory(exists=True, desc=(
"Output directory for coil phase images"))
class _BIDSGetInputSpec(BaseInterfaceInputSpec):
database_path = Directory(exists=True,
mandatory=True,
desc="Path to BIDS Dataset DBCACHE")
fixed_entities = traits.Dict(desc="Queries for outfield outputs")
align_volumes = traits.Either(
traits.Int,
None,
default=None,
desc="Run reference to align functional volumes",
)
class FeatureSpatialInputSpec(BaseInterfaceInputSpec):
melodic_ic_file = File(
exists=True,
desc=
"nii.gz file containing mixture-modelled thresholded (p>0.5) Z-maps, registered to the MNI152 2mm template"
)
aroma_dir = Directory(
exists=True,
desc=
"ICA-AROMA directory, containing the mask-files (mask_edge.nii.gz, mask_csf.nii.gz & mask_out.nii.gz)"
)
class BRAINSConstellationDetectorOutputSpec(TraitedSpec):
outputVolume = File(desc="ACPC-aligned output image with the same voxels, but updated origin, and direction cosign so that the AC point would fall at the physical location (0.0,0.0,0.0), and the mid-sagital plane is the plane where physical L/R coordinate is 0.0.", exists=True)
outputResampledVolume = File(desc="ACPC-aligned output image in a resampled unifor space. Currently this is a 1mm, 256^3, Identity direction image.", exists=True)
outputTransform = File(desc="The filename for the original space to ACPC alignment to be written (in .h5 format)., ", exists=True)
outputLandmarksInInputSpace = File(desc=", The filename for the new subject-specific landmark definition file in the same format produced by Slicer3 (.fcsv) with the landmarks in the original image space (the detected RP, AC, PC, and VN4) in it to be written., ", exists=True)
outputLandmarksInACPCAlignedSpace = File(desc=", The filename for the new subject-specific landmark definition file in the same format produced by Slicer3 (.fcsv) with the landmarks in the output image space (the detected RP, AC, PC, and VN4) in it to be written., ", exists=True)
outputMRML = File(desc=", The filename for the new subject-specific scene definition file in the same format produced by Slicer3 (in .mrml format). Only the components that were specified by the user on command line would be generated. Compatible components include inputVolume, outputVolume, outputLandmarksInInputSpace, outputLandmarksInACPCAlignedSpace, and outputTransform., ", exists=True)
outputVerificationScript = File(desc=", The filename for the Slicer3 script that verifies the aligned landmarks against the aligned image file. This will happen only in conjunction with saveOutputLandmarks and an outputVolume., ", exists=True)
outputUntransformedClippedVolume = File(desc="Output image in which to store neck-clipped input image, with the use of --acLowerBound and maybe --cutOutHeadInUntransformedVolume.", exists=True)
writeBranded2DImage = File(desc=", The filename for the 2D .png branded midline debugging image. This will happen only in conjunction with requesting an outputVolume., ", exists=True)
resultsDir = Directory(desc=", The directory for the debuging images to be written., ", exists=True)
class SubjectSummaryInputSpec(BaseInterfaceInputSpec):
t1w = InputMultiObject(File(exists=True), desc="T1w structural images")
t2w = InputMultiObject(File(exists=True), desc="T2w structural images")
subjects_dir = Directory(desc="FreeSurfer subjects directory")
subject_id = Str(desc="Subject ID")
dwi = InputMultiObject(
traits.Either(File(exists=True), traits.List(File(exists=True))),
desc="DWI files",
)
std_spaces = traits.List(Str, desc="list of standard spaces")
nstd_spaces = traits.List(Str, desc="list of non-standard spaces")
class BIDSDataSinkInputSpec(BaseInterfaceInputSpec):
base_directory = Directory(
mandatory=True,
desc='Path to BIDS (or derivatives) root directory')
in_file = InputMultiPath(File(exists=True), mandatory=True)
entities = InputMultiPath(traits.Dict, usedefault=True,
desc='Per-file entities to include in filename')
fixed_entities = traits.Dict(usedefault=True,
desc='Entities to include in all filenames')
path_patterns = InputMultiPath(
traits.Str, desc='BIDS path patterns describing format of file names')
class HipCombineChannelsInputSpec(BaseInterfaceInputSpec):
channels_dir = Directory(
exists=True,
desc=("Input directory containing real and imaginary images for each "
"channel."))
magnitude = File(genfile=True, desc="Combined magnitude image")
phase = File(genfile=True, desc="Combined phase image")
q = File(genfile=True, desc="Q image")
r2star = File(genfile=True, desc="R2* image")
echo_times = traits.List(traits.Float, mandatory=True, desc="Echo times")
class GifOutputSpec(TraitedSpec):
out_dir = Directory(desc='Output folder [default: ./]')
parc_file = File(desc='Parcellation file')
geo_file = File(desc='Geodesic distance file')
prior_file = File(desc='Prior file')
tiv_file = File(desc='Synthetic file')
seg_file = File(desc='Segmentation file')
brain_file = File(desc='Brain file')
bias_file = File(desc='Bias Corrected file')
synth_file = File(desc='Synthetic file')
class OutputSelectorInputSpec(BaseInterfaceInputSpec):
study_dir = Directory(
desc="The study dir. Should contain a subdir named after the subject.")
output_mni_only = traits.Bool(True,
mandatory=False, usedefault=True,
desc = """
To save space, by default we only output the pipeline's final
product. If you need to look at the precursor files, then just set
this to false... and prepare your drives to go to that big server
farm in the sky.
""")
class PostFSInputSpec(HCPCommandInputSpec):
full_command = traits.Str("PostFreeSurferPipeline.sh",
mandatory=True,
desc="Full path to the relevant HCP script.")
study_dir = Directory(
mandatory=True, exists=True, position=0, argstr='--path="%s"',
desc="The study dir. Should contain a subdir named after the subject.",)
subject = traits.Str(
mandatory=True, position=1, argstr='--subject="%s"',
desc="",)
surf_atlas_dir = Directory(
mandatory=True, exists=True, position=2, argstr='--surfatlasdir="%s"',
desc="",)
grayordinates_dir = Directory(
mandatory=True, exists=True, position=3, argstr='--grayordinatesdir="%s"',
desc="",)
grayordinates_res = traits.Int(2,
mandatory=False, usedefault=True, position=4, argstr='--grayordinatesres="%d"',
desc="""
Usually 2mm.""")
high_res_mesh = traits.Int(164,
mandatory=False, usedefault=True, position=5, argstr='--hiresmesh="%d"',
desc="Usually 164k vertices",)
low_res_mesh = traits.Int(32,
mandatory=False, usedefault=True, position=6, argstr='--lowresmesh="%d"',
desc="Usually 32k vertices",)
subcort_gray_labels = InputMultiPath(File(exists=True),
mandatory=True, position=7, argstr='--subcortgraylabels="%s"',
desc="")
freesurfer_labels = InputMultiPath(File(exists=True),
mandatory=True, position=8, argstr='--freesurferlabels="%s"',
desc="")
ref_myelin_maps = InputMultiPath(File(exists=True),
mandatory=True, position=9, argstr='--refmyelinmaps="%s"',
desc="")
reg_name = traits.Enum("FS", "MSMSulc", "MSMAll",
default="FS", mandatory=False, usedefault=True, position=10, argstr='--regname=%s',
desc="MSMSulc is recommended, if binary is not available use FS (FreeSurfer)")
print_com = traits.Enum("","echo",
mandatory=False, usedefault=True, position=11, argstr='--printcom=%s',
desc="")
class ClassTalairachVolumesInputSpec(CommandLineInputSpec):
brainMaskFile = File(argstr='%s',
desc="BrainMaskFile",
exists=True,
mandatory=True,
position=0)
classImageFile = File(argstr='%s',
desc="ClassImageFile",
exists=True,
mandatory=True,
position=1)
discreteImageFile = File(argstr='%s',
desc="DiscreteImageFile",
exists=True,
mandatory=True,
position=2)
talairachBounds = File(argstr='%s',
desc="TalairchBounds",
exists=True,
mandatory=True,
position=3)
talairachDir = Directory(argstr='%s',
desc="TalairachDir",
exists=True,
mandatory=True,
position=4)
patientId = traits.Str(argstr='%s',
desc="PatientID",
exists=True,
mandatory=True,
position=5)
scanId = traits.Str(argstr='%s',
desc="ScanId",
exists=True,
mandatory=True,
position=6)
resultDir = Directory(argstr='%s',
desc="ResultDir",
exists=True,
mandatory=True,
position=7)
class WatershedBEMInputSpec(FSTraitedSpec):
subject_id = traits.Str(argstr='--subject %s', mandatory=True,
desc='Subject ID (must have a complete Freesurfer directory)')
subjects_dir = Directory(exists=True, mandatory=True, usedefault=True,
desc='Path to Freesurfer subjects directory')
volume = traits.Enum('T1', 'aparc+aseg', 'aseg', 'brain', 'orig', 'brainmask', 'ribbon',
argstr='--volume %s', usedefault=True,
desc='The volume from the "mri" directory to use (defaults to T1)')
overwrite = traits.Bool(True, usedefault=True, argstr='--overwrite',
desc='Overwrites the existing files')
atlas_mode = traits.Bool(argstr='--atlas',
desc='Use atlas mode for registration (default: no rigid alignment)')
class BRAINSConstellationDetectorOutputSpec(TraitedSpec):
outputVolume = File(exists=True)
outputResampledVolume = File(exists=True)
outputTransform = File(exists=True)
outputLandmarksInInputSpace = File(exists=True)
outputLandmarksInACPCAlignedSpace = File(exists=True)
outputLandmarksPaired = File(exists=True)
outputMRML = File(exists=True)
outputVerificationScript = File(exists=True)
outputUntransformedClippedVolume = File(exists=True)
writeBranded2DImage = File(exists=True)
resultsDir = Directory(exists=True)
class DicomHeaderInfoExtractionOutputSpec(TraitedSpec):
tr = traits.Float(desc='Repetition time.')
start_time = traits.Str(desc='Scan start time.')
real_duration = traits.Str(desc='For 4D files, this will be the number of '
'volumes multiplied by the TR.')
tot_duration = traits.Str(
desc='Scan duration as extracted from the header.')
ped = traits.Str(desc='Phase encoding direction.')
pe_angle = traits.Str(desc='Phase angle.')
dcm_info = File(exists=True, desc='File with all the previous outputs.')
ref_motion_mats = Directory(desc='folder with the reference motion mats')
class InfantReconAllInputSpec(CommandLineInputSpec):
subjects_dir = Directory(
exists=True,
hash_files=False,
desc="path to subjects directory",
)
subject_id = traits.Str(
"recon_all",
argstr="--subject %s",
desc="subject name",
required=True,
)
t1_file = File(
exists=True,
desc="path to T1w file",
)
age = traits.Range(
low=0,
high=24,
argstr='--age %d',
desc="Subject age in months",
)
outdir = Directory(
argstr='--outdir %s',
desc="Output directory where the reconall results are written."
"The default location is <subjects_dir>/<subject_id>",
)
mask_file = traits.File(
argstr='--masked %s',
desc="Skull-stripped and INU-corrected T1 (skips skullstripping step)",
)
newborn = traits.Bool(
xor=['age'],
argstr='--newborn',
help="Use newborns from set",
)
aseg_file = File(
argstr='--segfile %s',
desc="Pre-computed segmentation file",
)
class BRAINSAlignMSPInputSpec(CommandLineInputSpec):
inputVolume = File( exists = True,argstr = "--inputVolume %s")
resampleMSP = traits.Either(traits.Bool, File(), hash_files = False,argstr = "--OutputresampleMSP %s")
verbose = traits.Bool( argstr = "--verbose ")
resultsDir = traits.Either(traits.Bool, Directory(), hash_files = False,argstr = "--resultsDir %s")
writedebuggingImagesLevel = traits.Int( argstr = "--writedebuggingImagesLevel %d")
mspQualityLevel = traits.Int( argstr = "--mspQualityLevel %d")
rescaleIntensities = traits.Bool( argstr = "--rescaleIntensities ")
trimRescaledIntensities = traits.Float( argstr = "--trimRescaledIntensities %f")
rescaleIntensitiesOutputRange = InputMultiPath(traits.Int, sep = ",",argstr = "--rescaleIntensitiesOutputRange %s")
backgroundFillValueString = traits.Str( argstr = "--BackgroundFillValue %s")
interpolationMode = traits.Enum("NearestNeighbor","Linear","ResampleInPlace","BSpline","WindowedSinc","Hamming","Cosine","Welch","Lanczos","Blackman", argstr = "--interpolationMode %s")
numberOfThreads = traits.Int( argstr = "--numberOfThreads %d")
class _GenerateCiftiInputSpec(BaseInterfaceInputSpec):
bold_file = File(mandatory=True, exists=True, desc="input BOLD file")
volume_target = traits.Enum("MNI152NLin6Asym", "MNI152NLin2009cAsym", usedefault=True,
desc="CIFTI volumetric output space")
surface_target = traits.Enum("fsLR", "fsaverage5", "fsaverage6", usedefault=True,
desc="CIFTI surface target space")
surface_density = traits.Enum('10k', '32k', '41k', '59k',
desc='Surface vertices density.')
TR = traits.Float(mandatory=True, desc="Repetition time")
surface_bolds = traits.List(File(exists=True), mandatory=True,
desc="list of surface BOLD GIFTI files"
" (length 2 with order [L,R])")
subjects_dir = Directory(mandatory=True, desc="FreeSurfer SUBJECTS_DIR")
class FileCheckInputSpec(BaseInterfaceInputSpec):
input_dir = Directory(exists=True, desc='Input directory to prepare properly.')
renaming = traits.Bool(False, desc='Whether or not to use the information stored'
'in the DICOM header to rename the subject and sessions '
'folders. If False, the file path will be splitted '
'and the subject name will be taken from there. In this '
'case, the subject_name_position must be provided.'
'Default is False.', usedefault=True)
subject_name_position = traits.Int(
-3, usedefault=True, desc='The position of the subject name in the splitted '
'file path (file_path.split("/")). Default is -3, so it assumes that the subject '
'name is in the third position starting from the end of the file path.')
class PrepareInputSpec(BaseInterfaceInputSpec):
in_dir = Directory(exists=True, mandatory=True)
base_filename = traits.Str(
value='T2swi3d_ axial_p2_0.9_iso_COSMOS_Straight_Coil',
mandatory=True,
desc='Base filename of coil files')
echo_times = traits.List(traits.Float(),
mandatory=True,
value=[20.0],
desc='Echo times in ms')
num_channels = traits.Int(value=32,
mandatory=True,
desc='Number of channels')
class PrepareFIXInputSpec(BaseInterfaceInputSpec):
melodic_dir = Directory()
filtered_epi = File(exists=True)
t1_brain = File(exists=True)
mc_par = File(exists=True)
epi_brain_mask = File(exists=True)
epi_preproc = File(exists=True)
epi2t1_mat = File(exists=True)
t12epi_mat = File(exists=True)
t12MNI_mat = File(exists=True)
MNI2t1_mat = File(exists=True)
epi_mean = File(exists=True)
class DcmStackInputSpec(NiftiGeneratorBaseInputSpec):
dicom_files = traits.Either(InputMultiPath(File(exists=True)),
Directory(exists=True),
traits.Str(),
mandatory=True)
embed_meta = traits.Bool(desc="Embed DICOM meta data into result")
exclude_regexes = traits.List(desc="Meta data to exclude, suplementing "
"any default exclude filters")
include_regexes = traits.List(desc="Meta data to include, overriding any "
"exclude filters")
force_read = traits.Bool(True,
usedefault=True,
desc=('Force reading files without DICM marker'))
class _FSInputSpecRPT(nrc._SVGReportCapableInputSpec):
bg_nii = File(exists=True,
usedefault=False,
resolve=True,
desc='Background NIFTI for SVG, will use T1.mgz if not '
'specified',
mandatory=False)
fs_dir = Directory(exists=True,
usedefault=False,
resolve=True,
desc='Subject freesurfer directory',
mandatory=True)
class CopyToDirInputSpec(TraitedSpec):
in_files = traits.List(traits.Either(File(exists=True),
Directory(exists=True)),
mandatory=True,
desc='input dicom files')
out_dir = File(desc='the output dicom file')
extension = traits.Str(desc='specify the extention for the copied file.',
default='',
usedefault=True)
file_names = traits.List(
traits.Str,
desc=("The filenames to use to save the files with within "
"the directory"))
class BRAINSAlignMSPInputSpec(CommandLineInputSpec):
inputVolume = File(argstr="--inputVolume %s", desc=", The Image to be resampled, ", exists=True)
OutputresampleMSP = traits.Either(traits.Bool, File(), argstr="--OutputresampleMSP %s", desc=", The image to be output., ", hash_files=False)
verbose = traits.Bool(argstr="--verbose ", desc=", Show more verbose output, ")
resultsDir = traits.Either(traits.Bool, Directory(), argstr="--resultsDir %s", desc=", The directory for the results to be written., ", hash_files=False)
writedebuggingImagesLevel = traits.Int(argstr="--writedebuggingImagesLevel %d", desc=", This flag controls if debugging images are produced. By default value of 0 is no images. Anything greater than zero will be increasing level of debugging images., ")
mspQualityLevel = traits.Int(argstr="--mspQualityLevel %d", desc=", Flag cotrols how agressive the MSP is estimated. 0=quick estimate (9 seconds), 1=normal estimate (11 seconds), 2=great estimate (22 seconds), 3=best estimate (58 seconds)., ")
rescaleIntensities = traits.Bool(argstr="--rescaleIntensities ", desc=", Flag to turn on rescaling image intensities on input., ")
trimRescaledIntensities = traits.Float(argstr="--trimRescaledIntensities %f", desc=", Turn on clipping the rescaled image one-tailed on input. Units of standard deviations above the mean. Very large values are very permissive. Non-positive value turns clipping off. Defaults to removing 0.00001 of a normal tail above the mean., ")
rescaleIntensitiesOutputRange = InputMultiPath(traits.Int, argstr="--rescaleIntensitiesOutputRange %s", desc=", This pair of integers gives the lower and upper bounds on the signal portion of the output image. Out-of-field voxels are taken from BackgroundFillValue., ", sep=",")
BackgroundFillValue = traits.Str(argstr="--BackgroundFillValue %s", desc="Fill the background of image with specified short int value. Enter number or use BIGNEG for a large negative number.")
interpolationMode = traits.Enum("NearestNeighbor", "Linear", "ResampleInPlace", "BSpline", "WindowedSinc", "Hamming", "Cosine", "Welch", "Lanczos", "Blackman", argstr="--interpolationMode %s", desc="Type of interpolation to be used when applying transform to moving volume. Options are Linear, ResampleInPlace, NearestNeighbor, BSpline, or WindowedSinc")
numberOfThreads = traits.Int(argstr="--numberOfThreads %d", desc="Explicitly specify the maximum number of threads to use.")
class SignalRegressionInputSpec(BaseInterfaceInputSpec):
fix_dir = Directory(exists=True, desc="Prepared FIX directory",
mandatory=True)
labelled_components = File(
exists=True, mandatory=True,
desc=("Text file with FIX classified components."))
motion_regression = traits.Bool(desc='Regress 24 motion paremeters.',
default=False)
highpass = traits.Float(
desc='Whether or not to high pass the motion parameters', default=None)
customRegressors = File(exists=True, default=None,
desc='File containing custom regressors.')
class FreesurferAtlasInputSpec(TraitedSpec):
freesurfer_dir = Directory(exists=True,
mandatory=True,
desc="Freesurfer directory after recon-all")
node_info = File('node_info.graphml',
usedefault=True,
desc='Output name for node info file')
roi_img = File('roi.nii.gz',
usedefault=True,
desc='Output name for roi image')
wm_img = File('wm.nii.gz',
usedefault=True,
desc='Output name for wm image')
class DistMatrixLatInputSpec(CommandLineInputSpec):
roi_file_a = File(exists=True,
argstr='-roia %s',
mandatory=True,
position=1,
desc='seed tracts a ID file')
roi_file_b = File(exists=True,
argstr='-roib %s',
mandatory=True,
position=2,
desc='seed tracts b ID file')
tract_dir_a = Directory(argstr='-traca %s',
mandatory=True,
position=3,
desc='tract a directory')
tract_dir_b = Directory(argstr='-tracb %s',
mandatory=True,
position=4,
desc='tract b directory')
memory = traits.Int(argstr="-mem %s",
mandatory=False,
desc="memory to be used (in Gb)")
threshold = traits.Float(argstr="-thr %s",
mandatory=True,
desc="threshold for tractogram values")
parallel = traits.Int(argstr="-nth %s",
mandatory=False,
desc="number of parallel processors to use")
verbose = traits.Bool(argstr="-v",
mandatory=False,
desc="use verbose output")
out_file = File(name_template="%s_distmat.v",
keep_extension=False,
argstr='-out %s',
hash_files=False,
position=-1,
desc='output distance matrix file',
name_source=["roi_file"])
