smriprep.workflows.outputs module

Writing outputs.

smriprep.workflows.outputs.init_anat_reports_wf(*, spaces, freesurfer, output_dir, sloppy=False, name='anat_reports_wf')

Set up a battery of datasinks to store reports in the right location.

Parameters:

• freesurfer (bool) – FreeSurfer was enabled

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: anat_reports_wf)

Inputs:

• source_file – Input T1w image

• std_t1w – T1w image resampled to standard space

• std_mask – Mask of skull-stripped template

• subject_dir – FreeSurfer SUBJECTS_DIR

• subject_id – FreeSurfer subject ID

• t1w_conform_report – Conformation report

• t1w_preproc – The T1w reference map, which is calculated as the average of bias-corrected and preprocessed T1w images, defining the anatomical space.

• t1w_dseg – Segmentation in T1w space

• t1w_mask – Brain (binary) mask estimated by brain extraction.

• template – Template space and specifications

smriprep.workflows.outputs.init_anat_second_derivatives_wf(*, bids_root: str, output_dir: str, cifti_output: Literal['91k', '170k', False], name='anat_second_derivatives_wf', tpm_labels=('GM', 'WM', 'CSF'))

Set up a battery of datasinks to store derivatives in the right location.

Parameters:

• bids_root (str) – Root path of BIDS dataset

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: anat_derivatives_wf)

• tpm_labels (tuple) – Tissue probability maps in order

Inputs:

• template – Template space and specifications

• source_files – List of input T1w images

• t1w_preproc – The T1w reference map, which is calculated as the average of bias-corrected and preprocessed T1w images, defining the anatomical space.

• t1w_mask – Mask of the t1w_preproc

• t1w_dseg – Segmentation in T1w space

• t1w_tpms – Tissue probability maps in T1w space

• anat2std_xfm – Nonlinear spatial transform to resample imaging data given in anatomical space into standard space.

• surfaces – GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)

• morphometrics – GIFTIs of cortical thickness, curvature, and sulcal depth

• t1w_fs_aseg – FreeSurfer’s aseg segmentation, in native T1w space

• t1w_fs_aparc – FreeSurfer’s aparc+aseg segmentation, in native T1w space

• cifti_morph – Morphometric CIFTI-2 dscalar files

• cifti_metadata – JSON files containing metadata dictionaries

smriprep.workflows.outputs.init_ds_anat_volumes_wf(*, bids_root: str, output_dir: str, name='ds_anat_volumes_wf', tpm_labels=('GM', 'WM', 'CSF')) → Workflow

smriprep.workflows.outputs.init_ds_dseg_wf(*, output_dir, name='ds_dseg_wf')

Save discrete segmentations

Parameters:

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: ds_dseg_wf)

Inputs:

• source_files – List of input T1w images

• t1w_dseg – Segmentation in T1w space

Outputs:

t1w_dseg – The location in the output directory of the discrete segmentation

smriprep.workflows.outputs.init_ds_fs_registration_wf(*, output_dir, name='ds_fs_registration_wf')

Save rigid registration between subject anatomical template and FreeSurfer T1.mgz

Parameters:

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: ds_fs_registration_wf)

Inputs:

• source_files – List of input T1w images

• fsnative2t1w_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

Outputs:

• t1w2fsnative_xfm – LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space

• fsnative2t1w_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

smriprep.workflows.outputs.init_ds_grayord_metrics_wf(*, bids_root: str, output_dir: str, metrics: list[str], cifti_output: Literal['91k', '170k'], name='ds_grayord_metrics_wf') → LiterateWorkflow

Save CIFTI-2 surface metrics

Parameters:

• bids_root (str) – Root path of BIDS dataset

• output_dir (str) – Directory in which to save derivatives

• metrics (str) – List of metrics to generate DataSinks for

• cifti_output (str) – Density of CIFTI-2 files to save

• name (str) – Workflow name (default: ds_surface_metrics_wf)

Inputs:

• source_files – List of input T1w images

• ``<metric>`` – CIFTI-2 scalar file for each metric passed to metrics

• ``<metric>_metadata`` – JSON file containing metadata for each metric passed to metrics

Outputs:

``<metric>`` – CIFTI-2 scalar file in output_dir for each metric passed to metrics

smriprep.workflows.outputs.init_ds_mask_wf(*, bids_root: str, output_dir: str, mask_type: str, name='ds_mask_wf')

Save the subject brain mask

Parameters:

• bids_root (str) – Root path of BIDS dataset

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: ds_mask_wf)

Inputs:

• source_files – List of input T1w images

• mask_file – Mask to save

Outputs:

mask_file – The location in the output directory of the mask

smriprep.workflows.outputs.init_ds_surface_metrics_wf(*, bids_root: str, output_dir: str, metrics: list[str], name='ds_surface_metrics_wf') → LiterateWorkflow

Save GIFTI surface metrics

Parameters:

• bids_root (str) – Root path of BIDS dataset

• output_dir (str) – Directory in which to save derivatives

• metrics (str) – List of metrics to generate DataSinks for

• name (str) – Workflow name (default: ds_surface_metrics_wf)

Inputs:

• source_files – List of input T1w images

• ``<metric>`` – Left and right GIFTIs for each metric passed to metrics

Outputs:

``<metric>`` – Left and right GIFTIs in output_dir for each metric passed to metrics

smriprep.workflows.outputs.init_ds_surfaces_wf(*, bids_root: str, output_dir: str, surfaces: list[str], name='ds_surfaces_wf') → LiterateWorkflow

Save GIFTI surfaces

Parameters:

• bids_root (str) – Root path of BIDS dataset

• output_dir (str) – Directory in which to save derivatives

• surfaces (str) – List of surfaces to generate DataSinks for

• name (str) – Workflow name (default: ds_surfaces_wf)

Inputs:

• source_files – List of input T1w images

• ``<surface>`` – Left and right GIFTIs for each surface passed to surfaces

Outputs:

``<surface>`` – Left and right GIFTIs in output_dir for each surface passed to surfaces

smriprep.workflows.outputs.init_ds_template_registration_wf(*, output_dir, name='ds_template_registration_wf')

Save template registration transforms

Parameters:

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: anat_derivatives_wf)

Inputs:

• template – Template space and specifications

• source_files – List of input T1w images

• anat2std_xfm – Nonlinear spatial transform to resample imaging data given in anatomical space into standard space.

• std2anat_xfm – Inverse transform of anat2std_xfm

smriprep.workflows.outputs.init_ds_template_wf(*, num_t1w, output_dir, name='ds_template_wf')

Save the subject-specific template

Parameters:

• num_t1w (int) – Number of T1w images

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: ds_template_wf)

Inputs:

• source_files – List of input T1w images

• t1w_ref_xfms – List of affine transforms to realign input T1w images

• t1w_preproc – The T1w reference map, which is calculated as the average of bias-corrected and preprocessed T1w images, defining the anatomical space.

Outputs:

t1w_preproc – The location in the output directory of the preprocessed T1w image

smriprep.workflows.outputs.init_ds_tpms_wf(*, output_dir, name='ds_tpms_wf', tpm_labels=('GM', 'WM', 'CSF'))

Save tissue probability maps

Parameters:

• output_dir (str) – Directory in which to save derivatives

• name (str) – Workflow name (default: anat_derivatives_wf)

• tpm_labels (tuple) – Tissue probability maps in order

Inputs:

• source_files – List of input T1w images

• t1w_tpms – Tissue probability maps in T1w space

Outputs:

t1w_tpms – The location in the output directory of the tissue probability maps

smriprep.workflows.outputs.init_template_iterator_wf(*, spaces: SpatialReferences, sloppy: bool = False, name='template_iterator_wf')

Prepare the necessary components to resample an image to a template space

This produces a workflow with an unjoined iterable named “spacesource”.

It takes as input a collated list of template specifiers and transforms to that space.

The fields in outputnode can be used as if they come from a single template.