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tsalotsalo
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Add normalization report
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3 files changed

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3 files changed

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Diff for: ‎src/fmripost_aroma/data/io_spec.json

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@@ -78,6 +78,18 @@
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"extension": [
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".tsv"
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]
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},
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"anat_dseg": {
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"datatype": "anat",
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"space": null,
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"res": null,
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"den": null,
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"desc": null,
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"suffix": "dseg",
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"extension": [
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".nii.gz",
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".nii"
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]
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}
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},
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"transforms": {

Diff for: ‎src/fmripost_aroma/workflows/base.py

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@@ -285,6 +285,7 @@ def init_single_run_wf(bold_file):
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from fmripost_aroma.utils.bids import collect_derivatives, extract_entities
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from fmripost_aroma.workflows.aroma import init_denoise_wf, init_ica_aroma_wf
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from fmripost_aroma.workflows.outputs import init_func_fit_reports_wf
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spaces = config.workflow.spaces
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omp_nthreads = config.nipype.omp_nthreads
@@ -294,7 +295,6 @@ def init_single_run_wf(bold_file):
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bold_metadata = config.execution.layout.get_metadata(bold_file)
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mem_gb = estimate_bold_mem_usage(bold_file)[1]
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ica_aroma_wf = init_ica_aroma_wf(bold_file=bold_file, metadata=bold_metadata, mem_gb=mem_gb)
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entities = extract_entities(bold_file)
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@@ -365,6 +365,8 @@ def init_single_run_wf(bold_file):
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)
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skip_vols = get_nss(functional_cache['confounds'])
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# Run ICA-AROMA
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ica_aroma_wf = init_ica_aroma_wf(bold_file=bold_file, metadata=bold_metadata, mem_gb=mem_gb)
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ica_aroma_wf.inputs.inputnode.confounds = functional_cache['confounds']
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ica_aroma_wf.inputs.inputnode.skip_vols = skip_vols
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@@ -479,6 +481,13 @@ def init_single_run_wf(bold_file):
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]),
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]) # fmt:skip
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# Generate reportlets
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func_fit_reports_wf = init_func_fit_reports_wf(output_dir=config.execution.output_dir)
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func_fit_reports_wf.inputs.inputnode.source_file = bold_file
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func_fit_reports_wf.inputs.inputnode.anat2std_xfm = functional_cache['anat2mni152nlin6asym']
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func_fit_reports_wf.inputs.inputnode.anat_dseg = functional_cache['anat_dseg']
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workflow.connect([(mni6_buffer, func_fit_reports_wf, [('bold', 'inputnode.bold_mni6')])])
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if config.workflow.denoise_method:
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# Now denoise the output-space BOLD data using ICA-AROMA
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denoise_wf = init_denoise_wf(bold_file=bold_file, metadata=bold_metadata)

Diff for: ‎src/fmripost_aroma/workflows/outputs.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,135 @@
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
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# Copyright The NiPreps Developers <[email protected]>
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# We support and encourage derived works from this project, please read
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# about our expectations at
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#
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# https://www.nipreps.org/community/licensing/
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#
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"""Writing out derivative files."""
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from __future__ import annotations
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from fmriprep.utils.bids import dismiss_echo
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from nipype.interfaces import utility as niu
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from nipype.pipeline import engine as pe
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from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
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from niworkflows.utils.images import dseg_label
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from fmripost_aroma.config import DEFAULT_MEMORY_MIN_GB
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from fmripost_aroma.interfaces.bids import DerivativesDataSink
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def init_func_fit_reports_wf(
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*,
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output_dir: str,
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name='func_fit_reports_wf',
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) -> pe.Workflow:
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"""Set up a battery of datasinks to store reports in the right location.
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Parameters
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----------
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freesurfer : :obj:`bool`
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FreeSurfer was enabled
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output_dir : :obj:`str`
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Directory in which to save derivatives
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name : :obj:`str`
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Workflow name (default: func_fit_reports_wf)
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Inputs
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------
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source_file
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Input BOLD images
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"""
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from nireports.interfaces.reporting.base import (
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SimpleBeforeAfterRPT as SimpleBeforeAfter,
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)
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from fmripost_aroma.interfaces.nilearn import MeanImage
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workflow = pe.Workflow(name=name)
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inputfields = [
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'source_file',
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'bold_mni6',
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'anat_dseg',
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'anat2std_xfm',
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]
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inputnode = pe.Node(niu.IdentityInterface(fields=inputfields), name='inputnode')
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# Average the BOLD image over time
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calculate_mean_bold = pe.Node(
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MeanImage(),
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name='calculate_mean_bold',
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mem_gb=1,
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)
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workflow.connect([(inputnode, calculate_mean_bold, [('bold_mni6', 'bold_file')])])
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# Warp the tissue segmentation to MNI
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dseg_to_mni6 = pe.Node(
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ApplyTransforms(interpolation='MultiLabel'),
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name='dseg_to_mni6',
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mem_gb=1,
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)
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workflow.connect([
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(inputnode, dseg_to_mni6, [
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('anat_dseg', 'input_image'),
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('anat2std_xfm', 'transforms'),
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('bold_mni6', 'reference_image'),
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]),
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]) # fmt:skip
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mni6_wm = pe.Node(
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niu.Function(function=dseg_label),
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name='mni6_wm',
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mem_gb=DEFAULT_MEMORY_MIN_GB,
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)
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mni6_wm.inputs.label = 2 # BIDS default is WM=2
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workflow.connect([(dseg_to_mni6, mni6_wm, [('output_image', 'in_file')])])
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# EPI-MNI registration
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epi_mni_report = pe.Node(
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SimpleBeforeAfter(
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before_label='T1w',
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after_label='EPI',
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dismiss_affine=True,
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),
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name='epi_mni_report',
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mem_gb=0.1,
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)
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workflow.connect([
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(inputnode, epi_mni_report, [('coreg_boldref', 'after')]),
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(calculate_mean_bold, epi_mni_report, [('out_file', 'before')]),
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(mni6_wm, epi_mni_report, [('output_image', 'wm_seg')]),
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]) # fmt:skip
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ds_epi_mni_report = pe.Node(
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DerivativesDataSink(
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base_directory=output_dir,
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desc='coreg',
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suffix='bold',
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datatype='figures',
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dismiss_entities=dismiss_echo(),
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),
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name='ds_epi_mni_report',
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)
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workflow.connect([
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(inputnode, ds_epi_mni_report, [('source_file', 'source_file')]),
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(epi_mni_report, ds_epi_mni_report, [('out_report', 'in_file')]),
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]) # fmt:skip
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return workflow

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