class IQMFileSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
in_file = Str(mandatory=True, desc='path of input file relative to BIDS root')
subject_id = Str(mandatory=True, desc='the subject id')
modality = Str(mandatory=True, desc='the qc type')
session_id = traits.Either(None, Str, usedefault=True)
task_id = traits.Either(None, Str, usedefault=True)
acq_id = traits.Either(None, Str, usedefault=True)
rec_id = traits.Either(None, Str, usedefault=True)
run_id = traits.Either(None, Str, usedefault=True)
dataset = Str(desc='dataset identifier')
metadata = traits.Dict()
provenance = traits.Dict()
root = traits.Dict(desc='output root dictionary')
out_dir = File(desc='the output directory')
_outputs = traits.Dict(value={}, usedefault=True)
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
else:
if key in self._outputs:
self._outputs[key] = value
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
class _DerivativesDataSinkInputSpec(DynamicTraitedSpec,
BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc='Path to the base directory for storing data.')
check_hdr = traits.Bool(True,
usedefault=True,
desc='fix headers of NIfTI outputs')
compress = traits.Bool(
desc="force compression (True) or uncompression (False)"
" of the output file (default: same as input)")
desc = Str('', usedefault=True, desc='Label for description field')
extra_values = traits.List(Str)
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc='the object to be saved')
keep_dtype = traits.Bool(False,
usedefault=True,
desc='keep datatype suffix')
meta_dict = traits.DictStrAny(
desc='an input dictionary containing metadata')
source_file = File(exists=False,
mandatory=True,
desc='the input func file')
space = Str('', usedefault=True, desc='Label for space field')
suffix = Str('', usedefault=True, desc='suffix appended to source_file')
class UploadIQMsInputSpec(BaseInterfaceInputSpec):
in_iqms = File(exists=True, mandatory=True, desc="the input IQMs-JSON file")
url = Str(mandatory=True, desc="URL (protocol and name) listening")
port = traits.Int(desc="MRIQCWebAPI service port")
path = Str(desc="MRIQCWebAPI endpoint root path")
email = Str(desc="set sender email")
strict = traits.Bool(
False, usedefault=True, desc="crash if upload was not succesfull"
)
class BIDSGrabInputSpec(BaseInterfaceInputSpec):
# TODO: Check this inteface, why are there 'either file or list'? ~Mateusz
fmri_prep_files = List()
fmri_prep_aroma_files = Either(List(ImageFile()), File())
conf_raw_files = Either(List(File(exists=True)), File(exists=True))
conf_json_files = Either(List(File(exists=True)), File(exists=True))
subject = Str()
task = Str()
session = Str()
run = Int()
class MaskToolInputSpec(AFNICommandInputSpec):
in_files = InputMultiPath(File(desc='input file to 3dmerge', exists=True),
argstr='-input %s',
position=-1,
mandatory=True,
copyfile=False)
out_file = File(name_template='%s_mask',
desc='output image file name',
argstr='-prefix %s',
name_source='in_files')
count = traits.Bool(
desc='Instead of created a binary 0/1 mask dataset, create one with '
'counts of voxel overlap, i.e., each voxel will contain the '
'number of masks that it is set in.',
argstr='-count',
position=2)
datum = traits.Enum(
'byte',
'short',
'float',
argstr='-datum %s',
desc='specify data type for output. Valid types are \'byte\', '
'\'short\' and \'float\'.')
dilate_inputs = Str(
desc='Use this option to dilate and/or erode datasets as they are '
'read. ex. \'5 -5\' to dilate and erode 5 times',
argstr='-dilate_inputs %s')
dilate_results = Str(
desc='dilate and/or erode combined mask at the given levels.',
argstr='-dilate_results %s')
frac = traits.Float(
desc='When combining masks (across datasets and sub-bricks), use '
'this option to restrict the result to a certain fraction of the '
'set of volumes',
argstr='-frac %s')
inter = traits.Bool(desc='intersection, this means -frac 1.0',
argstr='-inter')
union = traits.Bool(desc='union, this means -frac 0', argstr='-union')
fill_holes = traits.Bool(
desc='This option can be used to fill holes in the resulting mask, '
'i.e. after all other processing has been done.',
argstr='-fill_holes')
fill_dirs = Str(
desc='fill holes only in the given directions. This option is for use '
'with -fill holes. should be a single string that specifies '
'1-3 of the axes using {x,y,z} labels (i.e. dataset axis order), '
'or using the labels in {R,L,A,P,I,S}.',
argstr='-fill_dirs %s',
requires=['fill_holes'])
verbose = traits.Int(desc='specify verbosity level, for 0 to 3',
argstr='-verb %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')
fs_spaces = traits.List(desc='Target spaces')
template = InputMultiObject(Str, desc='Template space')
class MP2RAGESkullStripInputSpec(BaseInterfaceInputSpec):
second_inversion = File(
exists=True,
mandatory=True,
desc='Second inversion image derived from MP2RAGE sequence')
t1_weighted = File(exists=True,
mandatory=True,
desc='T1-weighted image derived from MP2RAGE sequence'
' (also referred to as “uniform” image) At least one of'
' t1_weighted and t1_map is required')
t1_map = File(
exists=True,
mandatory=True,
desc='Quantitative T1 map image derived from MP2RAGE'
'sequence At least one of t1_weighted and t1_map is required',
)
skip_zero_values = traits.Bool(
True,
use_defaul=True,
desc='Ignores voxels with zero value (default is True)')
topology_lut_dir = Directory(
desc='Path to directory in which topology files '
'are stored (default is stored in TOPOLOGY_LUT_DIR)')
prefix = Str(desc='Prefix of the resulting filenames')
class _DerivativesDataSinkInputSpec(DynamicTraitedSpec,
BaseInterfaceInputSpec):
base_directory = traits.Directory(
desc="Path to the base directory for storing data.")
check_hdr = traits.Bool(True,
usedefault=True,
desc="fix headers of NIfTI outputs")
compress = InputMultiObject(
traits.Either(None, traits.Bool),
usedefault=True,
desc=
"whether ``in_file`` should be compressed (True), uncompressed (False) "
"or left unmodified (None, default).",
)
data_dtype = Str(
desc="NumPy datatype to coerce NIfTI data to, or `source` to"
"match the input file dtype")
dismiss_entities = InputMultiObject(
traits.Either(None, Str),
usedefault=True,
desc="a list entities that will not be propagated from the source file",
)
in_file = InputMultiObject(File(exists=True),
mandatory=True,
desc="the object to be saved")
meta_dict = traits.DictStrAny(
desc="an input dictionary containing metadata")
source_file = InputMultiObject(
File(exists=False),
mandatory=True,
desc="the source file(s) to extract entities from")
class SubjectSummaryInputSpec(BaseInterfaceInputSpec):
t1w = InputMultiPath(File(exists=True), desc='T1w structural images')
t2w = InputMultiPath(File(exists=True), desc='T2w structural images')
subjects_dir = Directory(desc='FreeSurfer subjects directory')
subject_id = Str(desc='Subject ID')
dwi_groupings = traits.Dict(
desc='groupings of DWI files and their output names')
output_spaces = traits.List(desc='Target spaces')
template = traits.Enum('MNI152NLin2009cAsym', desc='Template space')
class SubjectSummaryInputSpec(BaseInterfaceInputSpec):
t1w = InputMultiPath(File(exists=True), desc='T1w structural images')
t2w = InputMultiPath(File(exists=True), desc='T2w structural images')
subjects_dir = Directory(desc='FreeSurfer subjects directory')
subject_id = Str(desc='Subject ID')
bold = InputMultiPath(traits.Either(File(exists=True),
traits.List(File(exists=True))),
desc='BOLD functional series')
output_spaces = traits.List(desc='Target spaces')
template = traits.Enum('MNI152NLin2009cAsym', desc='Template space')
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')
std_spaces = traits.List(Str, desc='list of standard spaces')
nstd_spaces = traits.List(Str, desc='list of non-standard spaces')
class TStatInputSpec(AFNICommandInputSpec):
in_file = File(
desc="input file to 3dTstat",
argstr="%s",
position=-1,
mandatory=True,
exists=True,
copyfile=False,
)
index = Str(desc="AFNI indexing string for in_file")
out_file = File(
name_template="%s_tstat",
desc="output image file name",
argstr="-prefix %s",
name_source="in_file",
)
mask = File(desc="mask file", argstr="-mask %s", exists=True)
options = Str(desc="selected statistical output", argstr="%s")
class ReadSidecarJSONOutputSpec(TraitedSpec):
subject_id = Str()
session_id = Str()
task_id = Str()
acq_id = Str()
rec_id = Str()
run_id = Str()
out_dict = traits.Dict()
relative_path = Str()
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 BIDSValidateInputSpec(BaseInterfaceInputSpec):
# Root directory only required argument
bids_dir = Directory(
exists=True,
required=True,
desc='BIDS dataset root directory'
)
# Default: 'fmriprep'
derivatives = traits.List(desc='Specifies name of derivatives directory')
# Separate queries from user
tasks = traits.List(Str(), desc='Names of tasks to denoise')
sessions = traits.List(Str(), desc='Labels of sessions to denoise')
subjects = traits.List(Str(), desc='Labels of subjects to denoise')
runs = traits.List(Int(), desc='Labels of runs to denoise')
# Pipelines from user or default
pipelines = traits.List(
File(),
desc='List of paths to selected pipelines'
)
class IQMFileSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
subject_id = Str(mandatory=True, desc='the subject id')
modality = Str(mandatory=True, desc='the qc type')
session_id = traits.Either(None, Str, usedefault=True)
task_id = traits.Either(None, Str, usedefault=True)
acq_id = traits.Either(None, Str, usedefault=True)
rec_id = traits.Either(None, Str, usedefault=True)
run_id = traits.Either(None, Str, usedefault=True)
md5sum = Str()
save_extra = traits.Bool(True, usedefault=True, desc='save extra metadata')
root = traits.Dict(desc='output root dictionary')
out_dir = File(desc='the output directory')
_outputs = traits.Dict(value={}, usedefault=True)
def __setattr__(self, key, value):
if key not in self.copyable_trait_names():
if not isdefined(value):
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
self._outputs[key] = value
else:
if key in self._outputs:
self._outputs[key] = value
super(IQMFileSinkInputSpec, self).__setattr__(key, value)
class CompositeTransformUtilInputSpec(ANTSCommandInputSpec):
process = traits.Enum(
'assemble',
'disassemble',
argstr='--%s',
position=1,
usedefault=True,
desc='What to do with the transform inputs (assemble or disassemble)',
)
in_file = InputMultiPath(File(exists=True),
mandatory=True,
argstr='%s...',
position=2,
desc='Input transform file(s)')
output_prefix = Str("transform",
usedefault=True,
argstr='%s',
position=3,
desc="A prefix that is prepended to all output files")
class _ROIsPlotInputSpecRPT(nrb._SVGReportCapableInputSpec):
in_file = File(
exists=True, mandatory=True, desc="the volume where ROIs are defined"
)
in_rois = InputMultiPath(
File(exists=True), mandatory=True, desc="a list of regions to be plotted"
)
in_mask = File(exists=True, desc="a special region, eg. the brain mask")
masked = traits.Bool(False, usedefault=True, desc="mask in_file prior plotting")
colors = traits.Either(
None, traits.List(Str), usedefault=True, desc="use specific colors for contours"
)
levels = traits.Either(
None,
traits.List(traits.Float),
usedefault=True,
desc="pass levels to nilearn.plotting",
)
mask_color = Str("r", usedefault=True, desc="color for mask")
class ROIsPlotInputSpecRPT(nrc.SVGReportCapableInputSpec):
in_file = File(exists=True,
mandatory=True,
desc='the volume where ROIs are defined')
in_rois = InputMultiPath(File(exists=True),
mandatory=True,
desc='a list of regions to be plotted')
in_mask = File(exists=True, desc='a special region, eg. the brain mask')
masked = traits.Bool(False,
usedefault=True,
desc='mask in_file prior plotting')
colors = traits.Either(None,
traits.List(Str),
usedefault=True,
desc='use specific colors for contours')
levels = traits.Either(None,
traits.List(traits.Float),
usedefault=True,
desc='pass levels to nilearn.plotting')
mask_color = Str('r', usedefault=True, desc='color for mask')
class _BIDSDataGrabberInputSpec(BaseInterfaceInputSpec):
subject_data = traits.Dict(Str, traits.Any)
subject_id = Str()
class TimecourseNormalizationInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, desc="functional image")
mask_file = File(exists=True, desc="mask image")
method = Str(desc="normalization method ('z' or 'psc')")
class SubjectSummaryOutputSpec(SummaryOutputSpec):
# This exists to ensure that the summary is run prior to the first ReconAll
# call, allowing a determination whether there is a pre-existing directory
subject_id = Str(desc='FreeSurfer subject ID')
class AboutSummaryInputSpec(BaseInterfaceInputSpec):
version = Str(desc='FMRIPREP version')
command = Str(desc='FMRIPREP command')
class AboutSummaryInputSpec(BaseInterfaceInputSpec):
version = Str(desc='dMRIPrep version')
command = Str(desc='dMRIPrep command')
class TopupSummaryInputSpec(BaseInterfaceInputSpec):
summary = Str(desc='Summary of TOPUP inputs')
class CAT12SegmentInputSpec(SPMCommandInputSpec):
in_files = InputMultiPath(
ImageFileSPM(exists=True),
field="data",
desc="file to segment",
mandatory=True,
copyfile=False,
)
_help_tpm = (
"Tissue Probability Map. Select the tissue probability image that includes 6 tissue probability "
"classes for (1) grey matter, (2) white matter, (3) cerebrospinal fluid, (4) bone, (5) non-brain "
"soft tissue, and (6) the background. CAT uses the TPM only for the initial SPM segmentation."
)
tpm = InputMultiPath(
ImageFileSPM(exists=True),
field="tpm",
desc=_help_tpm,
mandatory=False,
copyfile=False,
)
_help_shoots_tpm = (
"Shooting Template %d. The Shooting template must be in multi-volume nifti format and should contain GM,"
" WM, and background segmentations and have to be saved with at least 16 bit. "
)
shooting_tpm = ImageFileSPM(
exists=True,
field="extopts.registration.shooting.shootingtpm",
desc=_help_shoots_tpm % 0,
mandatory=False,
copyfile=False,
)
shooting_tpm_template_1 = ImageFileSPM(exists=True,
desc=_help_shoots_tpm % 1,
mandatory=False,
copyfile=False)
shooting_tpm_template_2 = ImageFileSPM(exists=True,
desc=_help_shoots_tpm % 2,
mandatory=False,
copyfile=False)
shooting_tpm_template_3 = ImageFileSPM(exists=True,
desc=_help_shoots_tpm % 3,
mandatory=False,
copyfile=False)
shooting_tpm_template_4 = ImageFileSPM(exists=True,
desc=_help_shoots_tpm % 4,
mandatory=False,
copyfile=False)
n_jobs = traits.Int(1,
usedefault=True,
mandatory=True,
field="nproc",
desc="Number of threads")
_help_affine_reg = (
"Affine Regularization. The procedure is a local optimisation, so it needs reasonable initial "
"starting estimates. Images should be placed in approximate alignment using the Display "
"function of SPM before beginning. A Mutual Information affine registration with the tissue "
"probability maps (D"
"Agostino et al, 2004) is used to achieve approximate alignment.")
affine_regularization = Str(default_value="mni",
field="opts.affreg",
usedefault=True,
desc=_help_affine_reg)
_help_bias_acc = (
"Strength of the SPM inhomogeneity (bias) correction that simultaneously controls the SPM "
"biasreg, biasfwhm, samp (resolution), and tol (iteration) parameter.")
power_spm_inhomogeneity_correction = traits.Float(default_value=0.5,
field="opts.biasacc",
usedefault=True,
desc=_help_bias_acc)
# Extended options for CAT12 preprocessing
_help_app = (
"Affine registration and SPM preprocessing can fail in some subjects with deviating anatomy (e.g. "
"other species/neonates) or in images with strong signal inhomogeneities, or untypical intensities "
"(e.g. synthetic images). An initial bias correction can help to reduce such problems (see details "
'below). Recommended are the "default" and "full" option.')
affine_preprocessing = traits.Int(1070,
field="extopts.APP",
desc=_help_app,
usedefault=True)
_help_initial_seg = (
"In rare cases the Unified Segmentation can fail in highly abnormal brains, where e.g. the "
"cerebrospinal fluid of superlarge ventricles (hydrocephalus) were classified as white "
"matter. However, if the affine registration is correct, the AMAP segmentation with an "
"prior-independent k-means initialization can be used to replace the SPM brain tissue "
"classification. Moreover, if the default Dartel and Shooting registrations will fail then "
'rhe "Optimized Shooting - superlarge ventricles" option for "Spatial registration" is ! '
"required Values: \nnone: 0;\nlight: 1;\nfull: 2;\ndefault: 1070.")
initial_segmentation = traits.Int(0,
field="extopts.spm_kamap",
desc=_help_initial_seg,
usedefault=True)
_help_las = (
"Additionally to WM-inhomogeneities, GM intensity can vary across different regions such as the motor"
" cortex, the basal ganglia, or the occipital lobe. These changes have an anatomical background "
"(e.g. iron content, myelinization), but are dependent on the MR-protocol and often lead to "
"underestimation of GM at higher intensities and overestimation of CSF at lower intensities. "
"Therefore, a local intensity transformation of all tissue classes is used to reduce these effects in"
" the image. This local adaptive segmentation (LAS) is applied before the final AMAP segmentation."
"Possible Values: \nSPM Unified Segmentation: 0 \nk-means AMAP: 2")
local_adaptive_seg = traits.Float(0.5,
field="extopts.LASstr",
usedefault=True,
desc=_help_las)
_help_gcutstr = (
"Method of initial skull-stripping before AMAP segmentation. The SPM approach works quite stable "
"for the majority of data. However, in some rare cases parts of GM (i.e. in frontal lobe) might "
"be cut. If this happens the GCUT approach is a good alternative. GCUT is a graph-cut/region-"
"growing approach starting from the WM area. APRG (adaptive probability region-growing) is a new"
" method that refines the probability maps of the SPM approach by region-growing techniques of "
"the gcut approach with a final surface-based optimization strategy. This is currently the method"
" with the most accurate and reliable results. If you use already skull-stripped data you can "
"turn off skull-stripping although this is automaticaly detected in most cases. Please note that "
"the choice of the skull-stripping method will also influence the estimation of TIV, because the"
" methods mainly differ in the handling of the outer CSF around the cortical surface. "
"\nPossible Values:\n - none (already skull-stripped): -1;\n - SPM approach: 0; "
"\n - GCUT approach: 0.50; \n - APRG approach: 2")
skull_strip = traits.Float(2,
field="extopts.gcutstr",
desc=_help_gcutstr,
usedefault=True)
_help_wmhc = (
"WARNING: Please note that the detection of WM hyperintensies is still under development and does "
"not have the same accuracy as approaches that additionally consider FLAIR images (e.g. Lesion "
"Segmentation Toolbox)! In aging or (neurodegenerative) diseases WM intensity can be reduced "
"locally in T1 or increased in T2/PD images. These so-called WM hyperintensies (WMHs) can lead to "
"preprocessing errors. Large GM areas next to the ventricle can cause normalization problems. "
"Therefore, a temporary correction for normalization is useful if WMHs are expected. CAT allows "
"different ways to handle WMHs: "
"\n0) No Correction (handled as GM). \n1) Temporary (internal) correction as WM for spatial "
"normalization and estimation of cortical thickness. \n2) Permanent correction to WM. "
)
wm_hyper_intensity_correction = traits.Int(1,
field="extopts.WMHC",
desc=_help_wmhc,
usedefault=True)
_help_vox = (
"The (isotropic) voxel sizes of any spatially normalised written images. A non-finite value will be "
"replaced by the average voxel size of the tissue probability maps used by the segmentation."
)
voxel_size = traits.Float(1.5,
field="extopts.vox",
desc=_help_vox,
usedefault=True)
_help_resampling = (
"Internal resampling for preprocessing.\n The default fixed image resolution offers a good "
"trade-off between optimal quality and preprocessing time and memory demands. Standard "
"structural data with a voxel resolution around 1mm or even data with high in-plane resolution"
" and large slice thickness (e.g. 0.5x0.5x1.5 mm) will benefit from this setting. If you have"
' higher native resolutions the highres option "Fixed 0.8 mm" will sometimes offer slightly'
" better preprocessing quality with an increase of preprocessing time and memory demands. In"
" case of even higher resolutions and high signal-to-noise ratio (e.g. for 7 T data) the "
'"Best native" option will process the data on the highest native resolution. A resolution'
" of 0.4x0.7x1.0 mm will be interpolated to 0.4x0.4x0.4 mm. A tolerance range of 0.1 mm is "
"used to avoid interpolation artifacts, i.e. a resolution of 0.95x1.01x1.08 mm will not be "
'interpolated in case of the "Fixed 1.0 mm"! This "optimal" option prefers an isotropic voxel '
"size with at least 1.1 mm that is controlled by the median voxel size and a volume term that "
"penalizes highly anisotropic voxels."
"Values:\nOptimal: [1.0 0.1]\nFixed 1.0 mm: [1.0 0.1];\nFixed 0.8 mm:[0.8 0.1]"
"\nBest native: [0.5 0.1]")
internal_resampling_process = traits.Tuple(
traits.Float(1),
traits.Float(0.1),
minlen=2,
maxlen=2,
usedefault=True,
field="extopts.restypes.optimal",
desc="help_resampling",
)
_errors_help = (
"Error handling.\nTry to catch preprocessing errors and continue with the next data set or ignore "
"all warnings (e.g., bad intensities) and use an experimental pipeline which is still in "
"development. In case of errors, CAT continues with the next subject if this option is enabled. If "
"the experimental option with backup functions is selected and warnings occur, CAT will try to use"
" backup routines and skip some processing steps which require good T1 contrasts (e.g., LAS). If "
"you want to avoid processing of critical data and ensure that only the main pipeline is used then"
' select the option "Ignore errors (continue with the next subject)". It is strongly recommended '
"to check for preprocessing problems, especially with non-T1 contrasts. "
"\nValues:\nnone: 0,\ndefault: 1,\ndetails: 2.")
ignore_errors = traits.Int(1,
field="extopts.ignoreErrors",
desc=_errors_help,
usedefault=True)
# Writing options
_help_surf = (
"Surface and thickness estimation. \nUse projection-based thickness (PBT) (Dahnke et al. 2012) to"
" estimate cortical thickness and to create the central cortical surface for left and right "
"hemisphere. Surface reconstruction includes topology correction (Yotter et al. 2011), spherical "
"inflation (Yotter et al.) and spherical registration. Additionally you can also estimate surface "
"parameters such as gyrification, cortical complexity or sulcal depth that can be subsequently "
"analyzed at each vertex of the surface. Please note, that surface reconstruction and spherical "
"registration additionally requires about 20-60 min of computation time. A fast (1-3 min) surface "
"pipeline is available for visual preview (e.g., to check preprocessing quality) in the "
"cross-sectional, but not in the longitudinal pipeline. Only the initial surfaces are created with "
"a lower resolution and without topology correction, spherical mapping and surface registration. "
"Please note that the files with the estimated surface thickness can therefore not be used for "
'further analysis! For distinction, these files contain "preview" in their filename and they'
" are not available as batch dependencies objects. ")
surface_and_thickness_estimation = traits.Int(1,
field="surface",
desc=_help_surf,
usedefault=True)
surface_measures = traits.Int(
1,
field="output.surf_measures",
usedefault=True,
desc="Extract surface measures",
)
# Templates
neuromorphometrics = traits.Bool(
True,
field="output.ROImenu.atlases.neuromorphometrics",
usedefault=True,
desc="Extract brain measures for Neuromorphometrics template",
)
lpba40 = traits.Bool(
True,
field="output.ROImenu.atlases.lpba40",
usedefault=True,
desc="Extract brain measures for LPBA40 template",
)
cobra = traits.Bool(
True,
field="output.ROImenu.atlases.hammers",
usedefault=True,
desc="Extract brain measures for COBRA template",
)
hammers = traits.Bool(
True,
field="output.ROImenu.atlases.cobra",
usedefault=True,
desc="Extract brain measures for Hammers template",
)
own_atlas = InputMultiPath(
ImageFileSPM(exists=True),
field="output.ROImenu.atlases.ownatlas",
desc="Extract brain measures for a given template",
mandatory=False,
copyfile=False,
)
# Grey matter
gm_output_native = traits.Bool(
False,
field="output.GM.native",
usedefault=True,
desc="Save modulated grey matter images.",
)
gm_output_modulated = traits.Bool(
True,
field="output.GM.mod",
usedefault=True,
desc="Save native grey matter images.",
)
gm_output_dartel = traits.Bool(
False,
field="output.GM.dartel",
usedefault=True,
desc="Save dartel grey matter images.",
)
# White matter
_wm_desc = "Options to save white matter images."
wm_output_native = traits.Bool(
False,
field="output.WM.native",
usedefault=True,
desc="Save dartel white matter images.",
)
wm_output_modulated = traits.Bool(
True,
field="output.WM.mod",
usedefault=True,
desc="Save dartel white matter images.",
)
wm_output_dartel = traits.Bool(
False,
field="output.WM.dartel",
usedefault=True,
desc="Save dartel white matter images.",
)
# CSF matter
_csf_desc = "Options to save CSF images."
csf_output_native = traits.Bool(
False,
field="output.CSF.native",
usedefault=True,
desc="Save dartel CSF images.",
)
csf_output_modulated = traits.Bool(True,
field="output.CSF.mod",
usedefault=True,
desc="Save dartel CSF images.")
csf_output_dartel = traits.Bool(
False,
field="output.CSF.dartel",
usedefault=True,
desc="Save dartel CSF images.",
)
# Labels
_help_label_desc = (
"This is the option to save a labeled version of your segmentations in the %s space for fast visual "
"comparision. Labels are saved as Partial Volume Estimation (PVE) values with different mix "
"classes for GM-WM (2.5) and GM-CSF (1.5). BG=0, CSF=1, GM=2, WM=3, WMH=4 (if WMHC=3), "
"SL=1.5 (if SLC)")
label_native = traits.Bool(
False,
field="output.label.native",
usedefault=True,
desc=_help_label_desc % "native",
)
label_warped = traits.Bool(
True,
field="output.label.warped",
usedefault=True,
desc=_help_label_desc % "warped",
)
label_dartel = traits.Bool(
False,
field="output.label.dartel",
usedefault=True,
desc=_help_label_desc % "dartel",
)
output_labelnative = traits.Bool(
False,
field="output.labelnative",
usedefault=True,
desc=_help_label_desc % "native",
)
# Bias
save_bias_corrected = traits.Bool(
True,
field="output.bias.warped",
usedefault=True,
desc="Save bias corrected image",
)
# las
_las_desc = (
"This is the option to save a bias, noise, and local intensity corrected version of the original T1"
" image in the %s space. MR images are usually corrupted by a smooth, spatially varying artifact that modulates the"
" intensity of the image (bias). These artifacts, although not usually a problem for visual "
"inspection, can impede automated processing of the images. The bias corrected version should have "
"more uniform intensities within the different types of tissues and can be saved in native space "
"and/or normalised. Noise is corrected by an adaptive non-local mean (NLM) filter (Manjon 2008, "
"Medical Image Analysis 12).")
las_native = traits.Bool(False,
field="output.las.native",
usedefault=True,
desc=_las_desc % "native")
las_warped = traits.Bool(True,
field="output.las.warped",
usedefault=True,
desc=_las_desc % "warped")
las_dartel = traits.Bool(False,
field="output.las.dartel",
usedefault=True,
desc=_las_desc % "dartel")
# Jacobian Warped
_help_jacobian = (
"This is the option to save the Jacobian determinant, which expresses local volume changes. This"
" image can be used in a pure deformation based morphometry (DBM) design. Please note that the"
" affine part of the deformation field is ignored. Thus, there is no need for any additional"
" correction for different brain sizes using ICV.")
jacobianwarped = traits.Bool(True,
field="output.jacobianwarped",
usedefault=True,
desc=_help_jacobian)
# Deformation Fields
_help_warp = (
"Deformation fields can be saved to disk, and used by the Deformations Utility and/or applied to "
"coregistered data from other modalities (e.g. fMRI). For spatially normalising images to MNI space,"
" you will need the forward deformation, whereas for spatially normalising (eg) GIFTI surface files,"
" you"
"ll need the inverse. It is also possible to transform data in MNI space on to the individual"
" subject, which also requires the inverse transform. Deformations are saved as .nii files, which"
" contain three volumes to encode the x, y and z coordinates."
"\nValues: No:[0 0];\nImage->Template (forward): [1 0];\nTemplate->Image (inverse): [0 1]; "
"\ninverse + forward: [1 1]")
warps = traits.Tuple(
traits.Int(1),
traits.Int(0),
minlen=2,
maxlen=2,
field="output.warps",
usedefault=True,
desc=_help_warp,
)
class _KeySelectInputSpec(DynamicTraitedSpec):
key = Str(mandatory=True, desc="selective key")
keys = InputMultiObject(Str, mandatory=True, min=1, desc="index of keys")
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")
output_spaces = InputMultiObject(Str, desc="list of standard spaces")
class _AboutSummaryInputSpec(BaseInterfaceInputSpec):
version = Str(desc="sMRIPrep version")
command = Str(desc="sMRIPrep command")
class _KeySelectOutputSpec(DynamicTraitedSpec):
key = Str(desc="propagates selected key")
