def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .dwi_dti_pipeline import DwiDti
pipeline = DwiDti(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(
plugin="MultiProc", plugin_args={"n_procs": args.n_procs}
)
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
self.name, pipeline.base_dir, pipeline.base_dir_was_specified
)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_label': args.group_label,
'acq_label': args.acq_label,
'suvr_reference_region': args.suvr_reference_region,
'pvc_psf_tsv': self.absolute_path(args.pvc_psf_tsv),
'mask_tissues': args.mask_tissues,
'mask_threshold': args.mask_threshold,
'pvc_mask_tissues': args.pvc_mask_tissues,
'smooth': args.smooth,
}
pipeline = PETVolume(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
caps_directory: str,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
) -> None:
"""DTI-based processing of DWI datasets.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/DWI_DTI/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .dwi_dti_pipeline import DwiDti
pipeline = DwiDti(
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
name=pipeline_name,
)
exec_pipeline = (
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": n_procs})
if n_procs
else pipeline.run()
)
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
pipeline_name, pipeline.base_dir, pipeline.base_dir_was_specified
)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .dwi_preprocessing_using_phasediff_fieldmap_pipeline import (
DwiPreprocessingUsingPhaseDiffFieldmap,
)
parameters = {
'low_bval': args.low_bval
}
pipeline = DwiPreprocessingUsingPhaseDiffFieldmap(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .pet_surface_pipeline import PetSurface
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'pet_tracer': args.pet_tracer, 'longitudinal': False}
pipeline = PetSurface(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .spatial_svm_pipeline import SpatialSVM
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_id': args.group_id,
'fwhm': args.fwhm,
'image_type': args.image_type,
'pet_tracer': args.pet_tracer,
'no_pvc': args.no_pvc
}
pipeline = SpatialSVM(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
caps_directory: str,
atlas_path: Optional[str] = None,
n_procs: Optional[int] = None,
) -> None:
"""Projection of the results of t1-freesurfer on another atlas.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_FreeSurfer/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .t1_freeesurfer_atlas_pipeline import T1FreeSurferAtlas
pipeline = T1FreeSurferAtlas(
caps_directory=caps_directory,
atlas_path=atlas_path,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def run_command(self, args):
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .statistics_volume_correction_pipeline import StatisticsVolumeCorrection
parameters = {
"t_map": args.t_map,
"height_threshold": args.height_threshold,
"FWEp": args.FWEp,
"FDRp": args.FDRp,
"FWEc": args.FWEp,
"FDRc": args.FDRp,
"n_cuts": args.n_cuts,
}
pipeline = StatisticsVolumeCorrection(
caps_directory=self.absolute_path(args.caps_directory),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .t1_freesurfer_pipeline import T1FreeSurfer
parameters = {
"recon_all_args": args.recon_all_args,
}
pipeline = T1FreeSurfer(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
overwrite_caps=args.overwrite_outputs,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
caps_directory: str,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
overwrite_outputs: bool = False,
) -> None:
"""Longitudinal pre-processing correction of T1w images with FreeSurfer.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_FreeSurfer_Longitudinal/"
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .t1_freesurfer_longitudinal_correction_pipeline import (
T1FreeSurferLongitudinalCorrection, )
pipeline = T1FreeSurferLongitudinalCorrection(
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
name=pipeline_name,
overwrite_caps=overwrite_outputs,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from fmri_preprocessing_pipeline import fMRIPreprocessing
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'full_width_at_half_maximum': args.full_width_at_half_maximum,
't1_native_space': args.t1_native_space,
'freesurfer_brain_mask': args.freesurfer_brain_mask,
'unwarping': args.unwarping,
}
pipeline = fMRIPreprocessing(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
# elif args.slurm:
# exec_pipeline = pipeline.run(plugin='SLURMGraph',
# plugin_args={'dont_resubmit_completed_jobs': True,
# 'sbatch_args': args.sbatch_args})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .t1_volume_parcellation_pipeline import T1VolumeParcellation
parameters = {
"group_label": args.group_label,
}
pipeline = T1VolumeParcellation(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .dwi_connectome_pipeline import DwiConnectome
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'n_tracks': args.n_tracks}
pipeline = DwiConnectome(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
from networkx import Graph
from .statistics_volume_pipeline import StatisticsVolume
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'contrast': args.contrast,
'feature_type': args.feature_type,
'group_id': args.group_id,
'custom_files': args.custom_files,
'cluster_threshold': args.cluster_threshold,
'group_id_caps': args.group_id_caps,
'full_width_at_half_maximum': args.full_width_at_half_maximum
}
pipeline = StatisticsVolume(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(
args.subject_visits_with_covariates_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_linear_pipeline import T1Linear
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {'crop_image': args.crop_image}
# Most of the time, you will want to instantiate your pipeline with a
# BIDS and CAPS directory as inputs:
pipeline = T1Linear(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'tissue_classes': args.tissue_classes,
'dartel_tissues': args.dartel_tissues,
'tissue_probability_maps': args.tissue_probability_maps,
'save_warped_unmodulated': not args.dont_save_warped_unmodulated,
'save_warped_modulated': args.save_warped_modulated,
}
pipeline = T1VolumeTissueSegmentation(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .pet_surface_pipeline import PetSurface
parameters = {
"acq_label": args.acq_label,
"suvr_reference_region": args.suvr_reference_region,
"pvc_psf_tsv": self.absolute_path(args.pvc_psf_tsv),
"longitudinal": False,
}
pipeline = PetSurface(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_register_dartel_pipeline import T1VolumeRegisterDartel
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_label': args.group_label,
'tissues': args.tissues,
}
pipeline = T1VolumeRegisterDartel(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from .t1_volume_parcellation_pipeline import T1VolumeParcellation
from clinica.utils.check_dependency import verify_cat12_atlases
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
# If the user wants to use any of the atlases of cat12 and has not installed it, we just remove it from the list
# of the computed atlases
args.atlases = verify_cat12_atlases(args.atlases)
parameters = {
'group_id': args.group_id,
'atlases': args.atlases,
}
pipeline = T1VolumeParcellation(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
caps_directory: str,
t_map: str,
height_threshold: str,
fwep: float,
fdrp: float,
fwec: float,
fdrc: float,
n_cuts: int = 8,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
) -> None:
"""Statistical correction of statistics-volume pipeline.
T_MAP is the name of the T statistic map used for the correction.
HEIGHT_THRESHOLD is the T value corresponding to an uncorrected p-value of 0.001.
FWEp is the Family-Wise Error peak threshold.
FDRp is the False Discovery Rate peak threshold.
Finally, FWEc and FDRc stand for the respective correction parameters.
Prerequisite: You have to perform the statistics-volume pipeline before running this pipeline.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/Stats_Volume/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .statistics_volume_correction_pipeline import StatisticsVolumeCorrection
parameters = {
"t_map": t_map,
"height_threshold": height_threshold,
"FWEp": fwep,
"FDRp": fdrp,
"FWEc": fwec,
"FDRc": fdrc,
"n_cuts": n_cuts,
}
pipeline = StatisticsVolumeCorrection(
caps_directory=caps_directory,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def cli(
bids_directory: str,
caps_directory: str,
acq_label: str,
suvr_reference_region: str,
pvc_psf_tsv: str,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
yes: bool = False,
) -> None:
"""Surface-based processing of PET images.
ACQ_LABEL corresponds to the label given to the PET acquisition, specifying the tracer used. Frequently used values are 'fdg'
or 'av45'.
The reference region must be precised to perform intensity normalization. Accepted values include: 'pons', 'cerebellumPons', 'pons2', 'cerebellumPons2'.
PVC_PSF_TSV is the TSV file containing the psf_x, psf_y and psf_z of the PSF for each PET image.
See https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/PET_Surface/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .pet_surface_pipeline import PetSurface
parameters = {
"acq_label": acq_label,
"suvr_reference_region": suvr_reference_region,
"pvc_psf_tsv": pvc_psf_tsv,
"longitudinal": False,
"skip_question": yes,
}
pipeline = PetSurface(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": n_procs})
if n_procs
else pipeline.run()
)
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
pipeline_name, pipeline.base_dir, pipeline.base_dir_was_specified
)
def cli(
ctx: click.Context,
bids_directory: str,
caps_directory: str,
recon_all_args: str,
working_directory: Optional[str] = None,
subjects_sessions_tsv: Optional[str] = None,
n_procs: Optional[int] = None,
overwrite_outputs: bool = False,
yes: bool = False,
atlas_path: Optional[str] = None,
) -> None:
"""Cross-sectional pre-processing of T1w images with FreeSurfer.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_FreeSurfer/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from ..t1_freesurfer_atlas import t1_freesurfer_atlas_cli
from .t1_freesurfer_pipeline import T1FreeSurfer
parameters = {"recon_all_args": recon_all_args, "skip_question": yes}
pipeline = T1FreeSurfer(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
overwrite_caps=overwrite_outputs,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
if atlas_path is not None:
ctx.invoke(
t1_freesurfer_atlas_cli.cli,
caps_directory=caps_directory,
atlas_path=atlas_path,
n_procs=n_procs,
)
def cli(
bids_directory: str,
caps_directory: str,
acq_label: str,
suvr_reference_region: str,
uncropped_image: bool = False,
save_pet_in_t1w_space: bool = False,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
) -> None:
"""Affine registration of PET images to the MNI standard space.
ACQ_LABEL corresponds the label given to the PET acquisition, specifying the tracer used. Frequently used values are 'fdg' or 'av45'.
The reference region must be precised to perform intensity normalization. Accepted values include: 'pons', 'cerebellumPons', 'pons2', 'cerebellumPons2'.
Prerequisite: You need to have performed the t1-linear pipeline on your T1-weighted MR images.
See https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/PET_Linear/"
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .pet_linear_pipeline import PETLinear
parameters = {
"acq_label": acq_label,
"suvr_reference_region": suvr_reference_region,
"uncropped_image": uncropped_image,
"save_PETinT1w": save_pet_in_t1w_space,
}
pipeline = PETLinear(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def run_command(self, args):
"""Run the pipeline with defined args."""
from colorama import Fore
from networkx import Graph
from clinica.utils.exceptions import ClinicaException
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .spatial_svm_pipeline import SpatialSVM
if args.orig_input_data == "pet-volume":
if args.acq_label is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --acq_label flag. "
f"Clinica will now exit.{Fore.RESET}")
if args.suvr_reference_region is None:
raise ClinicaException(
f"{Fore.RED}You selected pet-volume pipeline without setting --suvr_reference_region flag. "
f"Clinica will now exit.{Fore.RESET}")
parameters = {
# Clinica compulsory arguments
"group_label": args.group_label,
"orig_input_data": args.orig_input_data,
# Optional arguments for inputs from pet-volume pipeline
"acq_label": args.acq_label,
"use_pvc_data": args.use_pvc_data,
"suvr_reference_region": args.suvr_reference_region,
# Advanced arguments
"fwhm": args.full_width_half_maximum,
}
pipeline = SpatialSVM(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
bids_directory: str,
caps_directory: str,
group_label: str,
smooth: List[int] = (8, ),
tissues: List[int] = (1, 2, 3),
modulate: bool = True,
voxel_size: Tuple[float, float, float] = (1.5, 1.5, 1.5),
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
) -> None:
"""Register DARTEL template to MNI space.
GROUP_LABEL is an user-defined identifier to target a specific group of subjects. For this pipeline, it is associated to the DARTEL template that you had created when running the t1-volume pipeline.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_Volume/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .t1_volume_dartel2mni_pipeline import T1VolumeDartel2MNI
parameters = {
"group_label": group_label,
"tissues": tissues,
"voxel_size": voxel_size,
"modulate": modulate,
"smooth": smooth,
}
pipeline = T1VolumeDartel2MNI(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def cli(
bids_directory: str,
caps_directory: str,
tissue_classes: List[int] = (1, 2, 3),
dartel_tissues: List[int] = (1, 2, 3),
tissue_probability_maps: Optional[str] = None,
dont_save_warped_unmodulated: bool = False,
save_warped_modulated: bool = False,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
yes: bool = False,
) -> None:
"""Tissue segmentation, bias correction and spatial normalization to MNI space of T1w images with SPM.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_Volume/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .t1_volume_tissue_segmentation_pipeline import T1VolumeTissueSegmentation
parameters = {
"tissue_classes": tissue_classes,
"dartel_tissues": dartel_tissues,
"tissue_probability_maps": tissue_probability_maps,
"save_warped_unmodulated": not dont_save_warped_unmodulated,
"save_warped_modulated": save_warped_modulated,
"skip_question": yes,
}
pipeline = T1VolumeTissueSegmentation(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": n_procs})
if n_procs else pipeline.run())
if isinstance(exec_pipeline, Graph):
print_end_pipeline(pipeline_name, pipeline.base_dir,
pipeline.base_dir_was_specified)
def cli(
bids_directory: str,
caps_directory: str,
low_bval: int = 5,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
use_cuda: bool = False,
initrand: bool = False,
) -> None:
"""Preprocessing of raw DWI datasets using a T1w image.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/DWI_Preprocessing/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .dwi_preprocessing_using_t1_pipeline import DwiPreprocessingUsingT1
parameters = {
"low_bval": low_bval,
"use_cuda": use_cuda,
"initrand": initrand,
}
pipeline = DwiPreprocessingUsingT1(
bids_directory=bids_directory,
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": n_procs})
if n_procs
else pipeline.run()
)
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
pipeline_name, pipeline.base_dir, pipeline.base_dir_was_specified
)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.pipelines.pet_volume.pet_volume_pipeline import PETVolume
from clinica.utils.check_dependency import verify_cat12_atlases
from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
parameters = {
'group_id': args.group_id,
'psf_tsv': self.absolute_path(args.psf_tsv),
'pet_tracer': args.pet_tracer,
'mask_tissues': args.mask_tissues,
'mask_threshold': args.mask_threshold,
'pvc_mask_tissues': args.pvc_mask_tissues,
'smooth': args.smooth,
'atlases': args.atlases,
}
# If the user wants to use any of the atlases of CAT12 and has not installed it, we just remove it from the list
# of the computed atlases
parameters['atlases'] = verify_cat12_atlases(args.atlases)
pipeline = PETVolume(
bids_directory=self.absolute_path(args.bids_directory),
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name)
if args.n_procs:
exec_pipeline = pipeline.run(plugin='MultiProc',
plugin_args={'n_procs': args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def run_command(self, args):
"""Run the pipeline with defined args."""
from networkx import Graph
from clinica.utils.ux import print_crash_files_and_exit, print_end_pipeline
from .deeplearning_prepare_data_pipeline import DeepLearningPrepareData
parameters = {
# Add your own pipeline parameters here to use them inside your
# pipeline. See the file `deeplearning_prepare_data_pipeline.py` to
# see an example of use.
"modality": args.modality,
"extract_method": args.extract_method,
"patch_size": args.patch_size,
"stride_size": args.stride_size,
"slice_direction": args.slice_direction,
"slice_mode": args.slice_mode,
"use_uncropped_image": args.use_uncropped_image,
"custom_suffix": args.custom_suffix,
}
pipeline = DeepLearningPrepareData(
caps_directory=self.absolute_path(args.caps_directory),
tsv_file=self.absolute_path(args.subjects_sessions_tsv),
base_dir=self.absolute_path(args.working_directory),
parameters=parameters,
name=self.name,
)
if args.n_procs:
exec_pipeline = pipeline.run(plugin="MultiProc",
plugin_args={"n_procs": args.n_procs})
else:
exec_pipeline = pipeline.run()
if isinstance(exec_pipeline, Graph):
print_end_pipeline(self.name, pipeline.base_dir,
pipeline.base_dir_was_specified)
else:
print_crash_files_and_exit(args.logname, pipeline.base_dir)
def cli(
caps_directory: str,
group_label: str,
subjects_sessions_tsv: Optional[str] = None,
working_directory: Optional[str] = None,
n_procs: Optional[int] = None,
modulate: bool = True,
) -> None:
"""Computation of mean GM concentration for a set of regions.
GROUP_LABEL is an user-defined identifier to target a specific group of subjects. For this pipeline, it is associated to the DARTEL template that you had created when running the t1-volume pipeline.
https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/T1_Volume/
"""
from networkx import Graph
from clinica.utils.ux import print_end_pipeline
from .t1_volume_parcellation_pipeline import T1VolumeParcellation
parameters = {"group_label": group_label, "modulate": modulate}
pipeline = T1VolumeParcellation(
caps_directory=caps_directory,
tsv_file=subjects_sessions_tsv,
base_dir=working_directory,
parameters=parameters,
name=pipeline_name,
)
exec_pipeline = (
pipeline.run(plugin="MultiProc", plugin_args={"n_procs": n_procs})
if n_procs
else pipeline.run()
)
if isinstance(exec_pipeline, Graph):
print_end_pipeline(
pipeline_name, pipeline.base_dir, pipeline.base_dir_was_specified
)
