aslprep.workflows.asl.outputs module

Workflows for writing out derivative files.

init_asl_fit_reports_wf(*, sdc_correction: bool, freesurfer: bool, output_dir: str, name='asl_fit_reports_wf') → Workflow

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

Copied from fMRIPrep’s init_bold_fit_reports_wf. Modifications include changes to fields and variable names, which aren’t important, and DerivativesDataSink suffixes, which are.

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 BOLD images

• 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

init_ds_asl_native_wf(*, bids_root: str, output_dir: str, asl_output: bool, metadata: List[dict], cbf_3d: List[str], cbf_4d: List[str], att: List[str], name='ds_asl_native_wf') → Workflow

Write out aslref-space outputs.

init_ds_aslref_wf(*, bids_root, output_dir, desc: str, name='ds_aslref_wf') → Workflow

Write out aslref image.

init_ds_ciftis_wf(*, bids_root: str, output_dir: str, metadata: List[dict], cbf_3d: List[str], cbf_4d: List[str], att: List[str], omp_nthreads: int, name: str = 'ds_ciftis_wf') → Workflow

Apply transforms from reference to fsLR space and write out derivatives.

init_ds_volumes_wf(*, bids_root: str, output_dir: str, metadata: List[dict], cbf_3d: List[str], cbf_4d: List[str], att: List[str], name: str = 'ds_volumes_wf') → Workflow

Apply transforms from reference to anatomical/standard space and write out derivatives.

prepare_timing_parameters(metadata: dict)

Convert initial timing metadata to post-realignment timing metadata.

In particular, SliceTiming metadata is invalid once STC or any realignment is applied, as a matrix of voxels no longer corresponds to an acquisition slice. Therefore, if SliceTiming is present in the metadata dictionary, and a sparse acquisition paradigm is detected, DelayTime or AcquisitionDuration must be derived to preserve the timing interpretation.

Examples

If SliceTiming metadata is absent, then the only change is to note that STC has not been applied:

>>> prepare_timing_parameters(dict(RepetitionTime=2))
{"RepetitionTime": 2, "SliceTimingCorrected": False}
>>> prepare_timing_parameters(dict(RepetitionTime=2, DelayTime=0.5))
{"RepetitionTime": 2, "DelayTime": 0.5, "SliceTimingCorrected": False}
>>> prepare_timing_parameters(dict(VolumeTiming=[0.0, 1.0, 2.0, 5.0, 6.0, 7.0],
...                                AcquisitionDuration=1.0))  
{"VolumeTiming": [0.0, 1.0, 2.0, 5.0, 6.0, 7.0], "AcquisitionDuration": 1.0,
 "SliceTimingCorrected": False}

When SliceTiming is available and used, then SliceTimingCorrected is True and the StartTime indicates a series offset.

>>> with mock.patch("fmriprep.config.workflow.ignore", []):
...     prepare_timing_parameters(dict(RepetitionTime=2, SliceTiming=[0.0, 0.2, 0.4, 0.6]))
{"RepetitionTime": 2, "SliceTimingCorrected": True, "DelayTime": 1.2, "StartTime": 0.3}
>>> with mock.patch("fmriprep.config.workflow.ignore", []):
...     prepare_timing_parameters(
...         dict(VolumeTiming=[0.0, 1.0, 2.0, 5.0, 6.0, 7.0],
...              SliceTiming=[0.0, 0.2, 0.4, 0.6, 0.8]))  
{"VolumeTiming": [0.0, 1.0, 2.0, 5.0, 6.0, 7.0], "SliceTimingCorrected": True,
 "AcquisitionDuration": 1.0, "StartTime": 0.4}

When SliceTiming is available and not used, then SliceTimingCorrected is False and TA is indicated with DelayTime or AcquisitionDuration.

>>> with mock.patch("fmriprep.config.workflow.ignore", ["slicetiming"]):
...     prepare_timing_parameters(dict(RepetitionTime=2, SliceTiming=[0.0, 0.2, 0.4, 0.6]))
{"RepetitionTime": 2, "SliceTimingCorrected": False, "DelayTime": 1.2}
>>> with mock.patch("fmriprep.config.workflow.ignore", ["slicetiming"]):
...     prepare_timing_parameters(
...         dict(VolumeTiming=[0.0, 1.0, 2.0, 5.0, 6.0, 7.0],
...              SliceTiming=[0.0, 0.2, 0.4, 0.6, 0.8]))  
{"VolumeTiming": [0.0, 1.0, 2.0, 5.0, 6.0, 7.0], "SliceTimingCorrected": False,
 "AcquisitionDuration": 1.0}

If SliceTiming metadata is present but empty, then treat it as missing:

>>> with mock.patch("fmriprep.config.workflow.ignore", []):
...     prepare_timing_parameters(dict(RepetitionTime=2, SliceTiming=[]))
{"RepetitionTime": 2, "SliceTimingCorrected": False}
>>> with mock.patch("fmriprep.config.workflow.ignore", []):
...     prepare_timing_parameters(dict(RepetitionTime=2, SliceTiming=[0.0]))
{"RepetitionTime": 2, "SliceTimingCorrected": False}