def init_gifti_surface_wf(name='gifti_surface_wf'):
"""
Extract surfaces from FreeSurfer derivatives folder and
re-center GIFTI coordinates to align to native T1 space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']), name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(
MakeMidthickness(thickness=True, distance=0.5, out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list', run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file', name='fs_2_gii')
fix_surfs = pe.MapNode(NormalizeSurf(), iterfield='in_file', name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'),
('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs_2_gii')
def normalize_surfs(in_file):
""" Re-center GIFTI coordinates to fit align to native T1 space
For midthickness surfaces, add MidThickness metadata
Coordinate update based on:
https://github.com/Washington-University/workbench/blob/1b79e56/src/Algorithms/AlgorithmSurfaceApplyAffine.cxx#L73-L91
and
https://github.com/Washington-University/Pipelines/blob/ae69b9a/PostFreeSurfer/scripts/FreeSurfer2CaretConvertAndRegisterNonlinear.sh#L147
"""
import os
import numpy as np
import nibabel as nib
img = nib.load(in_file)
pointset = img.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0]
coords = pointset.data
c_ras_keys = ('VolGeomC_R', 'VolGeomC_A', 'VolGeomC_S')
ras = np.array([float(pointset.metadata[key]) for key in c_ras_keys])
# Apply C_RAS translation to coordinates
pointset.data = (coords + ras).astype(coords.dtype)
secondary = nib.gifti.GiftiNVPairs('AnatomicalStructureSecondary',
'MidThickness')
geom_type = nib.gifti.GiftiNVPairs('GeometricType', 'Anatomical')
has_ass = has_geo = False
for nvpair in pointset.meta.data:
# Remove C_RAS translation from metadata to avoid double-dipping in FreeSurfer
if nvpair.name in c_ras_keys:
nvpair.value = '0.000000'
# Check for missing metadata
elif nvpair.name == secondary.name:
has_ass = True
elif nvpair.name == geom_type.name:
has_geo = True
fname = os.path.basename(in_file)
# Update metadata for MidThickness/graymid surfaces
if 'midthickness' in fname.lower() or 'graymid' in fname.lower():
if not has_ass:
pointset.meta.data.insert(1, secondary)
if not has_geo:
pointset.meta.data.insert(2, geom_type)
img.to_filename(fname)
return os.path.abspath(fname)
fix_surfs = pe.MapNode(niu.Function(function=normalize_surfs),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out', 'surfaces')]),
])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
r"""
This workflow prepares GIFTI surfaces from a FreeSurfer subjects directory
If midthickness (or graymid) surfaces do not exist, they are generated and
saved to the subject directory as ``lh/rh.midthickness``.
These, along with the gray/white matter boundary (``lh/rh.smoothwm``), pial
sufaces (``lh/rh.pial``) and inflated surfaces (``lh/rh.inflated``) are
converted to GIFTI files.
Additionally, the vertex coordinates are :py:class:`recentered
<fmriprep.interfaces.NormalizeSurf>` to align with native T1w space.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_gifti_surface_wf
wf = init_gifti_surface_wf()
**Inputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
**Outputs**
surfaces
GIFTI surfaces for gray/white matter boundary, pial surface,
midthickness (or graymid) surface, and inflated surfaces
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs_2_gii')
fix_surfs = pe.MapNode(NormalizeSurf(),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def individual_reports(settings, name='ReportsWorkflow'):
"""
Encapsulates nodes writing plots
.. workflow::
from mriqc.workflows.functional import individual_reports
wf = individual_reports(settings={'output_dir': 'out'})
"""
from ..interfaces import PlotMosaic, PlotSpikes
from ..reports import individual_html
verbose = settings.get('verbose_reports', False)
biggest_file_gb = settings.get("biggest_file_size_gb", 1)
pages = 5
extra_pages = 0
if verbose:
extra_pages = 4
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_iqms', 'in_ras', 'hmc_epi', 'epi_mean', 'brainmask', 'hmc_fd',
'fd_thres', 'epi_parc', 'in_dvars', 'in_stddev', 'outliers',
'in_spikes', 'in_fft', 'mni_report', 'ica_report'
]),
name='inputnode')
# Set FD threshold
inputnode.inputs.fd_thres = settings.get('fd_thres', 0.2)
spmask = pe.Node(niu.Function(input_names=['in_file', 'in_mask'],
output_names=['out_file', 'out_plot'],
function=spikes_mask),
name='SpikesMask',
mem_gb=biggest_file_gb * 3.5)
spikes_bg = pe.Node(Spikes(no_zscore=True, detrend=False),
name='SpikesFinderBgMask',
mem_gb=biggest_file_gb * 2.5)
bigplot = pe.Node(FMRISummary(),
name='BigPlot',
mem_gb=biggest_file_gb * 3.5)
workflow.connect([
(inputnode, spikes_bg, [('in_ras', 'in_file')]),
(inputnode, spmask, [('in_ras', 'in_file')]),
(inputnode, bigplot, [('hmc_epi', 'in_func'), ('brainmask', 'in_mask'),
('hmc_fd', 'fd'), ('fd_thres', 'fd_thres'),
('in_dvars', 'dvars'), ('epi_parc', 'in_segm'),
('outliers', 'outliers')]),
(spikes_bg, bigplot, [('out_tsz', 'in_spikes_bg')]),
(spmask, spikes_bg, [('out_file', 'in_mask')]),
])
mosaic_mean = pe.Node(PlotMosaic(out_file='plot_func_mean_mosaic1.svg',
cmap='Greys_r'),
name='PlotMosaicMean')
mosaic_stddev = pe.Node(PlotMosaic(
out_file='plot_func_stddev_mosaic2_stddev.svg', cmap='viridis'),
name='PlotMosaicSD')
mplots = pe.Node(
niu.Merge(pages + extra_pages +
int(settings.get('fft_spikes_detector', False)) +
int(settings.get('ica', False))),
name='MergePlots')
rnode = pe.Node(niu.Function(input_names=['in_iqms', 'in_plots'],
output_names=['out_file'],
function=individual_html),
name='GenerateReport')
# Link images that should be reported
dsplots = pe.Node(nio.DataSink(base_directory=settings['output_dir'],
parameterization=False),
name='dsplots')
dsplots.inputs.container = 'reports'
workflow.connect([
(inputnode, rnode, [('in_iqms', 'in_iqms')]),
(inputnode, mosaic_mean, [('epi_mean', 'in_file')]),
(inputnode, mosaic_stddev, [('in_stddev', 'in_file')]),
(mosaic_mean, mplots, [('out_file', 'in1')]),
(mosaic_stddev, mplots, [('out_file', 'in2')]),
(bigplot, mplots, [('out_file', 'in3')]),
(mplots, rnode, [('out', 'in_plots')]),
(rnode, dsplots, [('out_file', '@html_report')]),
])
if settings.get('fft_spikes_detector', False):
mosaic_spikes = pe.Node(PlotSpikes(out_file='plot_spikes.svg',
cmap='viridis',
title='High-Frequency spikes'),
name='PlotSpikes')
workflow.connect([(inputnode, mosaic_spikes, [('in_ras', 'in_file'),
('in_spikes',
'in_spikes'),
('in_fft', 'in_fft')]),
(mosaic_spikes, mplots, [('out_file', 'in4')])])
if settings.get('ica', False):
page_number = 4
if settings.get('fft_spikes_detector', False):
page_number += 1
workflow.connect([(inputnode, mplots, [('ica_report',
'in%d' % page_number)])])
if not verbose:
return workflow
mosaic_zoom = pe.Node(PlotMosaic(out_file='plot_anat_mosaic1_zoomed.svg',
cmap='Greys_r'),
name='PlotMosaicZoomed')
mosaic_noise = pe.Node(PlotMosaic(out_file='plot_anat_mosaic2_noise.svg',
only_noise=True,
cmap='viridis_r'),
name='PlotMosaicNoise')
# Verbose-reporting goes here
from ..interfaces.viz import PlotContours
plot_bmask = pe.Node(PlotContours(display_mode='z',
levels=[.5],
colors=['r'],
cut_coords=10,
out_file='bmask'),
name='PlotBrainmask')
workflow.connect([
(inputnode, plot_bmask, [('epi_mean', 'in_file'),
('brainmask', 'in_contours')]),
(inputnode, mosaic_zoom, [('epi_mean', 'in_file'),
('brainmask', 'bbox_mask_file')]),
(inputnode, mosaic_noise, [('epi_mean', 'in_file')]),
(mosaic_zoom, mplots, [('out_file', 'in%d' % (pages + 1))]),
(mosaic_noise, mplots, [('out_file', 'in%d' % (pages + 2))]),
(plot_bmask, mplots, [('out_file', 'in%d' % (pages + 3))]),
(inputnode, mplots, [('mni_report', 'in%d' % (pages + 4))]),
])
return workflow
def individual_reports(settings, name='ReportsWorkflow'):
"""
Encapsulates nodes writing plots
.. workflow::
from mriqc.workflows.anatomical import individual_reports
wf = individual_reports(settings={'output_dir': 'out'})
"""
from ..interfaces import PlotMosaic
from ..reports import individual_html
verbose = settings.get('verbose_reports', False)
pages = 2
extra_pages = 0
if verbose:
extra_pages = 7
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_ras', 'brainmask', 'headmask', 'airmask', 'artmask', 'rotmask',
'segmentation', 'inu_corrected', 'noisefit', 'in_iqms', 'mni_report'
]),
name='inputnode')
mosaic_zoom = pe.Node(PlotMosaic(out_file='plot_anat_mosaic1_zoomed.svg',
title='zoomed',
cmap='Greys_r'),
name='PlotMosaicZoomed')
mosaic_noise = pe.Node(PlotMosaic(out_file='plot_anat_mosaic2_noise.svg',
title='noise enhanced',
only_noise=True,
cmap='viridis_r'),
name='PlotMosaicNoise')
mplots = pe.Node(niu.Merge(pages + extra_pages), name='MergePlots')
rnode = pe.Node(niu.Function(input_names=['in_iqms', 'in_plots'],
output_names=['out_file'],
function=individual_html),
name='GenerateReport')
# Link images that should be reported
dsplots = pe.Node(nio.DataSink(base_directory=settings['output_dir'],
parameterization=False),
name='dsplots')
dsplots.inputs.container = 'reports'
workflow.connect([
(inputnode, rnode, [('in_iqms', 'in_iqms')]),
(inputnode, mosaic_zoom, [('in_ras', 'in_file'),
('brainmask', 'bbox_mask_file')]),
(inputnode, mosaic_noise, [('in_ras', 'in_file')]),
(mosaic_zoom, mplots, [('out_file', "in1")]),
(mosaic_noise, mplots, [('out_file', "in2")]),
(mplots, rnode, [('out', 'in_plots')]),
(rnode, dsplots, [('out_file', "@html_report")]),
])
if not verbose:
return workflow
from ..interfaces.viz import PlotContours
from ..viz.utils import plot_bg_dist
plot_bgdist = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=plot_bg_dist),
name='PlotBackground')
plot_segm = pe.Node(PlotContours(display_mode='z',
levels=[.5, 1.5, 2.5],
cut_coords=10,
colors=['r', 'g', 'b']),
name='PlotSegmentation')
plot_bmask = pe.Node(PlotContours(display_mode='z',
levels=[.5],
colors=['r'],
cut_coords=10,
out_file='bmask'),
name='PlotBrainmask')
plot_airmask = pe.Node(PlotContours(display_mode='x',
levels=[.5],
colors=['r'],
cut_coords=6,
out_file='airmask'),
name='PlotAirmask')
plot_headmask = pe.Node(PlotContours(display_mode='x',
levels=[.5],
colors=['r'],
cut_coords=6,
out_file='headmask'),
name='PlotHeadmask')
plot_artmask = pe.Node(PlotContours(display_mode='z',
levels=[.5],
colors=['r'],
cut_coords=10,
out_file='artmask',
saturate=True),
name='PlotArtmask')
workflow.connect([
(inputnode, plot_segm, [('in_ras', 'in_file'),
('segmentation', 'in_contours')]),
(inputnode, plot_bmask, [('in_ras', 'in_file'),
('brainmask', 'in_contours')]),
(inputnode, plot_headmask, [('in_ras', 'in_file'),
('headmask', 'in_contours')]),
(inputnode, plot_airmask, [('in_ras', 'in_file'),
('airmask', 'in_contours')]),
(inputnode, plot_artmask, [('in_ras', 'in_file'),
('artmask', 'in_contours')]),
(inputnode, plot_bgdist, [('noisefit', 'in_file')]),
(inputnode, mplots, [('mni_report', "in%d" % (pages + 1))]),
(plot_bmask, mplots, [('out_file', 'in%d' % (pages + 2))]),
(plot_segm, mplots, [('out_file', 'in%d' % (pages + 3))]),
(plot_artmask, mplots, [('out_file', 'in%d' % (pages + 4))]),
(plot_headmask, mplots, [('out_file', 'in%d' % (pages + 5))]),
(plot_airmask, mplots, [('out_file', 'in%d' % (pages + 6))]),
(plot_bgdist, mplots, [('out_file', 'in%d' % (pages + 7))])
])
return workflow
