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enh: better selection of reference echo
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@oesteban
oesteban committed Nov 17, 2023
1 parent 9383268 commit 1c4a71b
Showing 1 changed file with 85 additions and 30 deletions.
115 changes: 85 additions & 30 deletions mriqc/workflows/functional/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,7 @@ def fmri_qc_workflow(name="funcMRIQC"):
"""
from nipype.algorithms.confounds import TSNR, NonSteadyStateDetector
from nipype.interfaces.afni import TStat
from niworkflows.interfaces.bids import ReadSidecarJSON
from niworkflows.interfaces.header import SanitizeImage
from mriqc.messages import BUILDING_WORKFLOW

Expand Down Expand Up @@ -101,6 +102,13 @@ def fmri_qc_workflow(name="funcMRIQC"):
name="outputnode",
)

# Get metadata
meta = pe.MapNode(ReadSidecarJSON(
index_db=config.execution.bids_database_dir
), name="metadata", iterfield=["in_file"])

pick_echo = pe.Node(niu.Function(select_echo), name="pick_echo")

non_steady_state_detector = pe.Node(NonSteadyStateDetector(), name="non_steady_state_detector")

sanitize = pe.MapNode(
Expand Down Expand Up @@ -146,14 +154,24 @@ def fmri_qc_workflow(name="funcMRIQC"):

workflow.connect([
(inputnode, datalad_get, [("in_file", "in_file")]),
(datalad_get, meta, [("in_file", "in_file")]),
(datalad_get, sanitize, [("in_file", "in_file")]),
(datalad_get, non_steady_state_detector, [(("in_file", select_echo2), "in_file")]),
(sanitize, pick_echo, [("out_file", "in_files")]),
(meta, pick_echo, [("EchoTime", "te_echos")]),
(pick_echo, non_steady_state_detector, [("out", "in_file")]),
(non_steady_state_detector, sanitize, [("n_volumes_to_discard", "n_volumes_to_discard")]),
(sanitize, hmcwf, [("out_file", "inputnode.in_file")]),
(hmcwf, mean, [("outputnode.out_file", "in_file")]),
(hmcwf, tsnr, [("outputnode.out_file", "in_file")]),
(mean, ema, [(("out_file", select_echo2), "inputnode.epi_mean")]),
(sanitize, ema, [(("out_file", _pop), "inputnode.epi_mean")]),
# Feed IQMs computation
(meta, iqmswf, [("out_dict", "inputnode.metadata"),
("subject", "subject"),
("session", "session"),
("task", "task"),
("acquisition", "acquisition"),
("reconstruction", "reconstruction"),
("run", "run")]),
(datalad_get, iqmswf, [("in_file", "inputnode.in_file")]),
(sanitize, iqmswf, [("out_file", "inputnode.in_ras")]),
(mean, iqmswf, [("out_file", "inputnode.epi_mean")]),
Expand All @@ -180,8 +198,8 @@ def fmri_qc_workflow(name="funcMRIQC"):
("outputnode.out_file", "inputnode.in_iqms"),
("outputnode.out_dvars", "inputnode.in_dvars"),
("outputnode.outliers", "inputnode.outliers"),
("outputnode.meta_sidecar", "inputnode.meta_sidecar"),
]),
(meta, func_report_wf, [("out_dict", "inputnode.meta_sidecar")]),
(hmcwf, outputnode, [("outputnode.out_fd", "out_fd")]),
])
# fmt: on
Expand All @@ -203,7 +221,7 @@ def fmri_qc_workflow(name="funcMRIQC"):
skullstrip_epi = fmri_bmsk_workflow(omp_nthreads=config.nipype.omp_nthreads)
# fmt: off
workflow.connect([
(mean, skullstrip_epi, [(("out_file", select_echo2), "inputnode.in_files")]),
(mean, skullstrip_epi, [(("out_file", _pop), "inputnode.in_files")]),
(skullstrip_epi, ema, [("outputnode.out_mask", "inputnode.epi_mask")]),
(skullstrip_epi, iqmswf, [("outputnode.out_mask", "inputnode.brainmask")]),
(skullstrip_epi, func_report_wf, [("outputnode.out_mask", "inputnode.brainmask")]),
Expand Down Expand Up @@ -262,7 +280,6 @@ def compute_iqms(name="ComputeIQMs"):
"""
from nipype.algorithms.confounds import ComputeDVARS
from nipype.interfaces.afni import OutlierCount, QualityIndex
from niworkflows.interfaces.bids import ReadSidecarJSON

from mriqc.interfaces import FunctionalQC, IQMFileSink
from mriqc.interfaces.reports import AddProvenance
Expand All @@ -285,6 +302,12 @@ def compute_iqms(name="ComputeIQMs"):
"in_tsnr",
"metadata",
"exclude_index",
"subject",
"session",
"task",
"acquisition",
"reconstruction",
"run",
]
),
name="inputnode",
Expand All @@ -297,7 +320,6 @@ def compute_iqms(name="ComputeIQMs"):
"outliers",
"out_spikes",
"out_fft",
"meta_sidecar",
]
),
name="outputnode",
Expand Down Expand Up @@ -366,11 +388,6 @@ def compute_iqms(name="ComputeIQMs"):
])
# fmt: on

# Add metadata
meta = pe.MapNode(ReadSidecarJSON(
index_db=config.execution.bids_database_dir
), name="metadata", iterfield=["in_file"])

addprov = pe.MapNode(
AddProvenance(modality="bold"),
name="provenance",
Expand All @@ -392,24 +409,22 @@ def compute_iqms(name="ComputeIQMs"):

# fmt: off
workflow.connect([
(inputnode, datasink, [("in_file", "in_file"),
("exclude_index", "dummy_trs")]),
(inputnode, meta, [("in_file", "in_file")]),
(inputnode, addprov, [("in_file", "in_file")]),
(meta, datasink, [(("subject", _pop), "subject_id"),
(("session", _pop), "session_id"),
(("task", _pop), "task_id"),
(("acquisition", _pop), "acq_id"),
(("reconstruction", _pop), "rec_id"),
(("run", _pop), "run_id"),
("out_dict", "metadata")]),
(inputnode, datasink, [("in_file", "in_file"),
("exclude_index", "dummy_trs"),
(("subject", _pop), "subject_id"),
(("session", _pop), "session_id"),
(("task", _pop), "task_id"),
(("acquisition", _pop), "acq_id"),
(("reconstruction", _pop), "rec_id"),
(("run", _pop), "run_id"),
("metadata", "metadata")]),
(addprov, datasink, [("out_prov", "provenance")]),
(outliers, datasink, [(("out_file", _parse_tout), "aor")]),
(gcor, datasink, [(("out", _tofloat), "gcor")]),
(quality, datasink, [(("out_file", _parse_tqual), "aqi")]),
(measures, datasink, [("out_qc", "root")]),
(datasink, outputnode, [("out_file", "out_file")]),
(meta, outputnode, [("out_dict", "meta_sidecar")]),
])
# fmt: on

Expand Down Expand Up @@ -761,7 +776,7 @@ def _apply_transforms(in_file, in_xfm):
return str(out_file)


def select_echo2(in_files):
def select_echo(in_files, te_echos=None, te=0.030):

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"""
Select the first file from a list of filenames.
Expand All @@ -771,12 +786,52 @@ def select_echo2(in_files):
Examples
--------
>>> select_file('some/file.nii.gz')
'some/file.nii.gz'
>>> select_file(['some/file1.nii.gz', 'some/file2.nii.gz'])
'some/file1.nii.gz'
>>> select_echo("single-echo.nii.gz")
'single-echo.nii.gz'
>>> select_echo(["single-echo.nii.gz"])
'single-echo.nii.gz'
>>> select_echo(
... [f"echo{n}.nii.gz" for n in range(1,7)],
... )
'echo2.nii.gz'
>>> select_echo(
... [f"echo{n}.nii.gz" for n in range(1,7)],
... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, 68.4],
... te=33.1,

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... )
'echo3.nii.gz'
>>> select_echo(
... [f"echo{n}.nii.gz" for n in range(1,7)],
... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1],
... te=33.1,

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... )
'echo2.nii.gz'
>>> select_echo(
... [f"echo{n}.nii.gz" for n in range(1,7)],
... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, None],
... te=33.1,

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... )
'echo2.nii.gz'
"""
if isinstance(in_files, (list, tuple)):
return in_files[min(1, len(in_files) - 1)]
return in_files
if not isinstance(in_files, (list, tuple)):
return in_files

if len(in_files) == 1:
return in_files[0]

import numpy as np

n_echos = len(in_files)
if te_echos is not None and len(te_echos) == n_echos:
try:
return in_files[np.argmin(np.abs(np.array(te_echos) - te))]

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except TypeError:
pass

return in_files[1]

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