def qc_snr_plot(wf, cfg, strat_pool, pipe_num, opt=None):
'''
{"name": "qc_snr_plot",
"config": ["pipeline_setup", "output_directory"],
"switch": ["generate_quality_control_images"],
"option_key": "None",
"option_val": "None",
"inputs": [(["desc-brain_bold", "desc-motion_bold", "desc-preproc_bold"],
"space-bold_desc-brain_mask"),
"from-bold_to-T1w_mode-image_desc-linear_xfm",
"desc-brain_T1w",
"space-T1w_desc-mean_bold"],
"outputs": ["bold-snr-axial-qc",
"bold-snr-sagittal-qc",
"bold-snr-hist-qc",
"bold-snr-qc"]}
'''
# make SNR plot
qc_workflow = create_qc_snr(f'qc_snr_{pipe_num}')
node, out = strat_pool.get_data(
["desc-brain_bold", "desc-motion_bold", "desc-preproc_bold"])
wf.connect(node, out, qc_workflow, 'inputspec.functional_preprocessed')
node, out = strat_pool.get_data("space-bold_desc-brain_mask")
wf.connect(node, out, qc_workflow, 'inputspec.functional_brain_mask')
node, out = \
strat_pool.get_data('from-bold_to-T1w_mode-image_desc-linear_xfm')
wf.connect(node, out, qc_workflow,
'inputspec.functional_to_anat_linear_xfm')
node, out = strat_pool.get_data('desc-brain_T1w')
wf.connect(node, out, qc_workflow, 'inputspec.anatomical_brain')
node, out = strat_pool.get_data('space-T1w_desc-mean_bold')
wf.connect(node, out, qc_workflow, 'inputspec.mean_functional_in_anat')
outputs = {
'bold-snr-axial-qc': (qc_workflow, 'outputspec.snr_axial_image'),
'bold-snr-sagittal-qc': (qc_workflow, 'outputspec.snr_sagittal_image'),
'bold-snr-hist-qc': (qc_workflow, 'outputspec.snr_histogram_image'),
'bold-snr-qc': (qc_workflow, 'outputspec.snr_mean')
}
return (wf, outputs)
def create_qc_workflow(workflow, c, strategies, qc_outputs):
qc_montage_id_a = {}
qc_montage_id_s = {}
qc_plot_id = {}
qc_hist_id = {}
for num_strat, strat in enumerate(strategies):
nodes = strat.get_nodes_names()
if 'functional_preprocessed' in strat:
preproc, out_file = strat['functional_preprocessed']
brain_mask, mask_file = strat['functional_brain_mask']
func_to_anat_xfm, xfm_file = strat['functional_to_anat_linear_xfm']
anat_ref, ref_file = strat['anatomical_brain']
mfa, mfa_file = strat['mean_functional_in_anat']
# make SNR plot
qc_workflow = create_qc_snr('qc_snr_{0}'.format(num_strat))
workflow.connect(preproc, out_file, qc_workflow,
'inputspec.functional_preprocessed')
workflow.connect(brain_mask, mask_file, qc_workflow,
'inputspec.functional_brain_mask')
workflow.connect(func_to_anat_xfm, xfm_file, qc_workflow,
'inputspec.functional_to_anat_linear_xfm')
workflow.connect(anat_ref, ref_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(mfa, mfa_file, qc_workflow,
'inputspec.mean_functional_in_anat')
strat.update_resource_pool({
'qc___snr_a': (qc_workflow, 'outputspec.snr_axial_image'),
'qc___snr_s': (qc_workflow, 'outputspec.snr_sagittal_image'),
'qc___snr_hist':
(qc_workflow, 'outputspec.snr_histogram_image'),
'qc___snr_val': (qc_workflow, 'outputspec.snr_mean')
})
if not 0 in qc_montage_id_a:
qc_montage_id_a[0] = 'snr_a'
qc_montage_id_s[0] = 'snr_s'
qc_hist_id[0] = 'snr_hist'
# make motion parameters plot
mov_param, out_file = strat['movement_parameters']
qc_workflow = create_qc_motion('qc_motion_{0}'.format(num_strat))
workflow.connect(mov_param, out_file, qc_workflow,
'inputspec.motion_parameters')
strat.update_resource_pool({
'qc___movement_trans_plot':
(qc_workflow, 'outputspec.motion_translation_plot'),
'qc___movement_rot_plot':
(qc_workflow, 'outputspec.motion_rotation_plot')
})
if not 1 in qc_plot_id:
qc_plot_id[1] = 'movement_trans_plot'
if not 2 in qc_plot_id:
qc_plot_id[2] = 'movement_rot_plot'
# make FD plot and volumes removed
if ('gen_motion_stats' in nodes or 'gen_motion_stats_before_stc'
in nodes) and 1 in c.runNuisance:
fd, out_file = strat['frame_wise_displacement_jenkinson']
qc_workflow = create_qc_fd('qc_fd_{0}'.format(num_strat))
workflow.connect(fd, out_file, qc_workflow, 'inputspec.fd')
strat.update_resource_pool({
'qc___fd_plot':
(qc_workflow, 'outputspec.fd_histogram_plot')
})
if not 3 in qc_plot_id:
qc_plot_id[3] = 'fd_plot'
# make QC montages for Skull Stripping Visualization
anat_underlay, out_file = strat['anatomical_brain']
skull, out_file_s = strat['anatomical_reorient']
qc_workflow = create_qc_skullstrip(
'qc_skullstrip_{0}'.format(num_strat))
workflow.connect(anat_underlay, out_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(skull, out_file_s, qc_workflow,
'inputspec.anatomical_reorient')
strat.update_resource_pool({
'qc___skullstrip_vis_a': (qc_workflow, 'outputspec.axial_image'),
'qc___skullstrip_vis_s': (qc_workflow, 'outputspec.sagittal_image')
})
if not 4 in qc_montage_id_a:
qc_montage_id_a[4] = 'skullstrip_vis_a'
qc_montage_id_s[4] = 'skullstrip_vis_s'
if 'anatomical_to_standard' in strat:
# make QC montages for mni normalized anatomical image
mni_anat_underlay, out_file = strat['anatomical_to_standard']
montage_mni_anat = create_montage(
'montage_mni_anat_{0}'.format(num_strat), 'red', 'mni_anat')
workflow.connect(mni_anat_underlay, out_file, montage_mni_anat,
'inputspec.underlay')
template_brain_for_anat, out_file = strat[
'template_brain_for_anat']
anat_template_edge = pe.Node(
Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='anat_template_edge_{0}'.format(num_strat))
workflow.connect(template_brain_for_anat, out_file,
anat_template_edge, 'in_file')
workflow.connect(anat_template_edge, 'out_file', montage_mni_anat,
'inputspec.overlay')
strat.update_resource_pool({
'qc___mni_normalized_anatomical_a':
(montage_mni_anat, 'outputspec.axial_png'),
'qc___mni_normalized_anatomical_s':
(montage_mni_anat, 'outputspec.sagittal_png')
})
if not 5 in qc_montage_id_a:
qc_montage_id_a[5] = 'mni_normalized_anatomical_a'
qc_montage_id_s[5] = 'mni_normalized_anatomical_s'
# make QC montages for CSF WM GM
if 'seg_preproc' in nodes:
anat_underlay, out_file_anat = strat['anatomical_brain']
csf_overlay, out_file_csf = strat['anatomical_csf_mask']
wm_overlay, out_file_wm = strat['anatomical_wm_mask']
gm_overlay, out_file_gm = strat['anatomical_gm_mask']
montage_csf_gm_wm = create_montage_gm_wm_csf(
'montage_csf_gm_wm_%d' % num_strat, 'montage_csf_gm_wm')
workflow.connect(anat_underlay, out_file_anat, montage_csf_gm_wm,
'inputspec.underlay')
workflow.connect(csf_overlay, out_file_csf, montage_csf_gm_wm,
'inputspec.overlay_csf')
workflow.connect(wm_overlay, out_file_wm, montage_csf_gm_wm,
'inputspec.overlay_wm')
workflow.connect(gm_overlay, out_file_gm, montage_csf_gm_wm,
'inputspec.overlay_gm')
strat.update_resource_pool({
'qc___csf_gm_wm_a':
(montage_csf_gm_wm, 'outputspec.axial_png'),
'qc___csf_gm_wm_s':
(montage_csf_gm_wm, 'outputspec.sagittal_png')
})
if not 7 in qc_montage_id_a:
qc_montage_id_a[7] = 'csf_gm_wm_a'
qc_montage_id_s[7] = 'csf_gm_wm_s'
if 'functional_preprocessed' in strat:
preproc, out_file_preproc = strat['functional_to_standard']
mean_preproc, out_file_mean_preproc = strat[
'mean_functional_to_standard']
# make QC Carpet plot
carpet_seg = create_qc_carpet('carpet_seg_%d' % num_strat,
'carpet_seg')
workflow.connect(preproc, out_file_preproc, carpet_seg,
'inputspec.functional_to_standard')
workflow.connect(mean_preproc, out_file_mean_preproc,
carpet_seg,
'inputspec.mean_functional_to_standard')
workflow.connect(c.PRIORS_GRAY, 'local_path', carpet_seg,
'inputspec.anatomical_gm_mask')
workflow.connect(c.PRIORS_WHITE, 'local_path', carpet_seg,
'inputspec.anatomical_wm_mask')
workflow.connect(c.PRIORS_CSF, 'local_path', carpet_seg,
'inputspec.anatomical_csf_mask')
strat.update_resource_pool({
'qc___carpet': (carpet_seg, 'outputspec.carpet_plot'),
})
if not 8 in qc_plot_id:
qc_plot_id[8] = 'carpet'
if 'functional_preprocessed' in strat:
# make QC montage for Mean Functional in T1 with T1 edge
anat, out_file = strat['anatomical_brain']
m_f_a, out_file_mfa = strat['mean_functional_in_anat']
anat_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='anat_edge_%d' % num_strat)
montage_anat = create_montage('montage_anat_%d' % num_strat, 'red',
't1_edge_on_mean_func_in_t1')
workflow.connect(anat, out_file, anat_edge, 'in_file')
workflow.connect(anat_edge, 'out_file', montage_anat,
'inputspec.overlay')
workflow.connect(m_f_a, out_file_mfa, montage_anat,
'inputspec.underlay')
strat.update_resource_pool({
'qc___mean_func_with_t1_edge_a':
(montage_anat, 'outputspec.axial_png'),
'qc___mean_func_with_t1_edge_s':
(montage_anat, 'outputspec.sagittal_png')
})
if not 9 in qc_montage_id_a:
qc_montage_id_a[9] = 'mean_func_with_t1_edge_a'
qc_montage_id_s[9] = 'mean_func_with_t1_edge_s'
# make QC montage for Mean Functional in MNI with MNI edge
m_f_i, out_file = strat['mean_functional_to_standard']
montage_mfi = create_montage('montage_mfi_%d' % num_strat, 'red',
'MNI_edge_on_mean_func_mni')
workflow.connect(m_f_i, out_file, montage_mfi,
'inputspec.underlay')
template_brain_for_func, out_file = strat[
'template_brain_for_func_preproc']
func_template_edge = pe.Node(
Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='func_template_edge_{0}'.format(num_strat))
workflow.connect(template_brain_for_func, out_file,
func_template_edge, 'in_file')
workflow.connect(func_template_edge, 'out_file', montage_mfi,
'inputspec.overlay')
strat.update_resource_pool({
'qc___mean_func_with_mni_edge_a':
(montage_mfi, 'outputspec.axial_png'),
'qc___mean_func_with_mni_edge_s':
(montage_mfi, 'outputspec.sagittal_png')
})
if not 10 in qc_montage_id_a:
qc_montage_id_a[10] = 'mean_func_with_mni_edge_a'
qc_montage_id_s[10] = 'mean_func_with_mni_edge_s'
# Link all the derivatives to the QC pages
idx = 11
rp = strat.get_resource_pool()
for key in sorted(rp.keys()):
# qc_outputs is from the outputs CSV
if key in qc_outputs:
qa_montages(workflow, c, strat, num_strat, qc_montage_id_a,
qc_montage_id_s, qc_hist_id, key, idx)
idx += 1
return qc_montage_id_a, qc_montage_id_s, qc_hist_id, qc_plot_id
def create_qc_workflow(workflow, c, strategies, qc_outputs):
qc_montage_id_a = {}
qc_montage_id_s = {}
qc_plot_id = {}
qc_hist_id = {}
for num_strat, strat in enumerate(strategies):
nodes = strat.get_nodes_names()
preproc, out_file = strat['functional_preprocessed']
brain_mask, mask_file = strat['functional_brain_mask']
func_to_anat_xfm, xfm_file = strat['functional_to_anat_linear_xfm']
anat_ref, ref_file = strat['anatomical_brain']
mfa, mfa_file = strat['mean_functional_in_anat']
# make SNR plot
qc_workflow = create_qc_snr('qc_snr_{0}'.format(num_strat))
workflow.connect(preproc, out_file, qc_workflow,
'inputspec.functional_preprocessed')
workflow.connect(brain_mask, mask_file, qc_workflow,
'inputspec.functional_brain_mask')
workflow.connect(func_to_anat_xfm, xfm_file, qc_workflow,
'inputspec.functional_to_anat_linear_xfm')
workflow.connect(anat_ref, ref_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(mfa, mfa_file, qc_workflow,
'inputspec.mean_functional_in_anat')
strat.update_resource_pool({
'qc___snr_a': (qc_workflow, 'outputspec.snr_axial_image'),
'qc___snr_s': (qc_workflow, 'outputspec.snr_sagittal_image'),
'qc___snr_hist': (qc_workflow, 'outputspec.snr_histogram_image'),
'qc___snr_val': (qc_workflow, 'outputspec.snr_mean')
})
if not 3 in qc_montage_id_a:
qc_montage_id_a[3] = 'snr_a'
qc_montage_id_s[3] = 'snr_s'
qc_hist_id[3] = 'snr_hist'
# make motion parameters plot
mov_param, out_file = strat['movement_parameters']
qc_workflow = create_qc_motion('qc_motion_{0}'.format(num_strat))
workflow.connect(mov_param, out_file, qc_workflow,
'inputspec.motion_parameters')
strat.update_resource_pool({
'qc___movement_trans_plot':
(qc_workflow, 'outputspec.motion_translation_plot'),
'qc___movement_rot_plot':
(qc_workflow, 'outputspec.motion_rotation_plot')
})
if not 6 in qc_plot_id:
qc_plot_id[6] = 'movement_trans_plot'
if not 7 in qc_plot_id:
qc_plot_id[7] = 'movement_rot_plot'
# make FD plot and volumes removed
if 'gen_motion_stats' in nodes and 1 in c.runNuisance:
if c.fdCalc == 'Power':
fd, out_file = strat['frame_wise_displacement_power']
else:
fd, out_file = strat['frame_wise_displacement_jenkinson']
qc_workflow = create_qc_fd('qc_fd_{0}'.format(num_strat))
workflow.connect(fd, out_file, qc_workflow, 'inputspec.fd')
if "De-Spiking" in c.runMotionSpike:
excluded, out_file_ex = strat['despiking_frames_excluded']
workflow.connect(excluded, out_file_ex, qc_workflow,
'inputspec.excluded_volumes')
elif "Scrubbing" in c.runMotionSpike:
excluded, out_file_ex = strat['scrubbing_frames_excluded']
workflow.connect(excluded, out_file_ex, qc_workflow,
'inputspec.excluded_volumes')
strat.update_resource_pool({
'qc___fd_plot': (qc_workflow, 'outputspec.fd_histogram_plot')
})
if not 8 in qc_plot_id:
qc_plot_id[8] = 'fd_plot'
# make QC montages for Skull Stripping Visualization
anat_underlay, out_file = strat['anatomical_brain']
skull, out_file_s = strat['anatomical_reorient']
qc_workflow = create_qc_skullstrip(
'qc_skullstrip_{0}'.format(num_strat))
workflow.connect(anat_underlay, out_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(skull, out_file_s, qc_workflow,
'inputspec.anatomical_reorient')
strat.update_resource_pool({
'qc___skullstrip_vis_a': (qc_workflow, 'outputspec.axial_image'),
'qc___skullstrip_vis_s': (qc_workflow, 'outputspec.sagittal_image')
})
if not 1 in qc_montage_id_a:
qc_montage_id_a[1] = 'skullstrip_vis_a'
qc_montage_id_s[1] = 'skullstrip_vis_s'
# make QC montages for mni normalized anatomical image
mni_anat_underlay, out_file = strat['mean_functional_in_anat']
montage_mni_anat = create_montage(
'montage_mni_anat_{0}'.format(num_strat), 'red', 'mni_anat')
workflow.connect(mni_anat_underlay, out_file, montage_mni_anat,
'inputspec.underlay')
montage_mni_anat.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'))
strat.update_resource_pool({
'qc___mni_normalized_anatomical_a':
(montage_mni_anat, 'outputspec.axial_png'),
'qc___mni_normalized_anatomical_s':
(montage_mni_anat, 'outputspec.sagittal_png')
})
if not 6 in qc_montage_id_a:
qc_montage_id_a[6] = 'mni_normalized_anatomical_a'
qc_montage_id_s[6] = 'mni_normalized_anatomical_s'
# make QC montages for CSF WM GM
if 'seg_preproc' in nodes:
anat_underlay, out_file = strat['anatomical_brain']
csf_overlay, out_file_csf = strat['anatomical_csf_mask']
wm_overlay, out_file_wm = strat['anatomical_wm_mask']
gm_overlay, out_file_gm = strat['anatomical_gm_mask']
montage_csf_gm_wm = create_montage_gm_wm_csf(
'montage_csf_gm_wm_%d' % num_strat, 'montage_csf_gm_wm')
workflow.connect(anat_underlay, out_file, montage_csf_gm_wm,
'inputspec.underlay')
workflow.connect(csf_overlay, out_file_csf, montage_csf_gm_wm,
'inputspec.overlay_csf')
workflow.connect(wm_overlay, out_file_wm, montage_csf_gm_wm,
'inputspec.overlay_wm')
workflow.connect(gm_overlay, out_file_gm, montage_csf_gm_wm,
'inputspec.overlay_gm')
strat.update_resource_pool({
'qc___csf_gm_wm_a':
(montage_csf_gm_wm, 'outputspec.axial_png'),
'qc___csf_gm_wm_s':
(montage_csf_gm_wm, 'outputspec.sagittal_png')
})
if not 2 in qc_montage_id_a:
qc_montage_id_a[2] = 'csf_gm_wm_a'
qc_montage_id_s[2] = 'csf_gm_wm_s'
# make QC montage for Mean Functional in T1 with T1 edge
anat, out_file = strat['anatomical_brain']
m_f_a, out_file_mfa = strat['mean_functional_in_anat']
anat_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='anat_edge_%d' % num_strat)
montage_anat = create_montage('montage_anat_%d' % num_strat, 'red',
't1_edge_on_mean_func_in_t1')
workflow.connect(anat, out_file, anat_edge, 'in_file')
workflow.connect(anat_edge, 'out_file', montage_anat,
'inputspec.overlay')
workflow.connect(m_f_a, out_file_mfa, montage_anat,
'inputspec.underlay')
strat.update_resource_pool({
'qc___mean_func_with_t1_edge_a':
(montage_anat, 'outputspec.axial_png'),
'qc___mean_func_with_t1_edge_s':
(montage_anat, 'outputspec.sagittal_png')
})
if not 4 in qc_montage_id_a:
qc_montage_id_a[4] = 'mean_func_with_t1_edge_a'
qc_montage_id_s[4] = 'mean_func_with_t1_edge_s'
# make QC montage for Mean Functional in MNI with MNI edge
m_f_i, out_file = strat['mean_functional_to_standard']
montage_mfi = create_montage('montage_mfi_%d' % num_strat, 'red',
'MNI_edge_on_mean_func_mni')
workflow.connect(m_f_i, out_file, montage_mfi, 'inputspec.underlay')
montage_mfi.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'))
strat.update_resource_pool({
'qc___mean_func_with_mni_edge_a':
(montage_mfi, 'outputspec.axial_png'),
'qc___mean_func_with_mni_edge_s':
(montage_mfi, 'outputspec.sagittal_png')
})
if not 5 in qc_montage_id_a:
qc_montage_id_a[5] = 'mean_func_with_mni_edge_a'
qc_montage_id_s[5] = 'mean_func_with_mni_edge_s'
# Link all the derivatives to the QC pages
idx = 7
rp = strat.get_resource_pool()
for key in sorted(rp.keys()):
# qc_outputs is from the outputs CSV
if key in qc_outputs:
qa_montages(workflow, c, strat, num_strat, qc_montage_id_a,
qc_montage_id_s, qc_hist_id, key, idx)
idx += 1
return qc_montage_id_a, qc_montage_id_s, qc_hist_id, qc_plot_id
def create_qc_workflow(workflow, c, strategies, qc_outputs):
qc_montage_id_a = {}
qc_montage_id_s = {}
qc_plot_id = {}
qc_hist_id = {}
for num_strat, strat in enumerate(strategies):
nodes = strat.get_nodes_names()
if 'functional_preprocessed' in strat:
preproc, out_file = strat['functional_preprocessed']
brain_mask, mask_file = strat['functional_brain_mask']
func_to_anat_xfm, xfm_file = strat['functional_to_anat_linear_xfm']
anat_ref, ref_file = strat['anatomical_brain']
mfa, mfa_file = strat['mean_functional_in_anat']
# make SNR plot
qc_workflow = create_qc_snr('qc_snr_{0}'.format(num_strat))
workflow.connect(preproc, out_file, qc_workflow, 'inputspec.functional_preprocessed')
workflow.connect(brain_mask, mask_file, qc_workflow, 'inputspec.functional_brain_mask')
workflow.connect(func_to_anat_xfm, xfm_file, qc_workflow, 'inputspec.functional_to_anat_linear_xfm')
workflow.connect(anat_ref, ref_file, qc_workflow, 'inputspec.anatomical_brain')
workflow.connect(mfa, mfa_file, qc_workflow, 'inputspec.mean_functional_in_anat')
strat.update_resource_pool({
'qc___snr_a': (qc_workflow, 'outputspec.snr_axial_image'),
'qc___snr_s': (qc_workflow, 'outputspec.snr_sagittal_image'),
'qc___snr_hist': (qc_workflow, 'outputspec.snr_histogram_image'),
'qc___snr_val': (qc_workflow, 'outputspec.snr_mean')
})
if not 3 in qc_montage_id_a:
qc_montage_id_a[3] = 'snr_a'
qc_montage_id_s[3] = 'snr_s'
qc_hist_id[3] = 'snr_hist'
# make motion parameters plot
mov_param, out_file = strat['movement_parameters']
qc_workflow = create_qc_motion('qc_motion_{0}'.format(num_strat))
workflow.connect(mov_param, out_file, qc_workflow,
'inputspec.motion_parameters')
strat.update_resource_pool({
'qc___movement_trans_plot': (qc_workflow, 'outputspec.motion_translation_plot'),
'qc___movement_rot_plot': (qc_workflow, 'outputspec.motion_rotation_plot')
})
if not 6 in qc_plot_id:
qc_plot_id[6] = 'movement_trans_plot'
if not 7 in qc_plot_id:
qc_plot_id[7] = 'movement_rot_plot'
# make FD plot and volumes removed
if 'gen_motion_stats' in nodes and 1 in c.runNuisance:
fd, out_file = strat['frame_wise_displacement_jenkinson']
qc_workflow = create_qc_fd('qc_fd_{0}'.format(num_strat))
workflow.connect(fd, out_file, qc_workflow, 'inputspec.fd')
strat.update_resource_pool({
'qc___fd_plot': (qc_workflow, 'outputspec.fd_histogram_plot')
})
if not 8 in qc_plot_id:
qc_plot_id[8] = 'fd_plot'
# make QC montages for Skull Stripping Visualization
anat_underlay, out_file = strat['anatomical_brain']
skull, out_file_s = strat['anatomical_reorient']
qc_workflow = create_qc_skullstrip(
'qc_skullstrip_{0}'.format(num_strat)
)
workflow.connect(anat_underlay, out_file,
qc_workflow, 'inputspec.anatomical_brain')
workflow.connect(skull, out_file_s,
qc_workflow, 'inputspec.anatomical_reorient')
strat.update_resource_pool({
'qc___skullstrip_vis_a': (qc_workflow, 'outputspec.axial_image'),
'qc___skullstrip_vis_s': (qc_workflow, 'outputspec.sagittal_image')
})
if not 1 in qc_montage_id_a:
qc_montage_id_a[1] = 'skullstrip_vis_a'
qc_montage_id_s[1] = 'skullstrip_vis_s'
if 'functional_preprocessed' in strat:
# make QC montages for mni normalized anatomical image
mni_anat_underlay, out_file = strat['mean_functional_in_anat']
montage_mni_anat = create_montage('montage_mni_anat_{0}'.format(num_strat),
'red',
'mni_anat')
workflow.connect(mni_anat_underlay, out_file,
montage_mni_anat, 'inputspec.underlay')
montage_mni_anat.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'
)
)
strat.update_resource_pool({'qc___mni_normalized_anatomical_a': (montage_mni_anat, 'outputspec.axial_png'),
'qc___mni_normalized_anatomical_s': (montage_mni_anat, 'outputspec.sagittal_png')})
if not 6 in qc_montage_id_a:
qc_montage_id_a[6] = 'mni_normalized_anatomical_a'
qc_montage_id_s[6] = 'mni_normalized_anatomical_s'
# make QC montages for CSF WM GM
if 'seg_preproc' in nodes:
anat_underlay, out_file_anat = strat['anatomical_brain']
csf_overlay, out_file_csf = strat['anatomical_csf_mask']
wm_overlay, out_file_wm = strat['anatomical_wm_mask']
gm_overlay, out_file_gm = strat['anatomical_gm_mask']
montage_csf_gm_wm = create_montage_gm_wm_csf('montage_csf_gm_wm_%d' % num_strat,
'montage_csf_gm_wm')
workflow.connect(anat_underlay, out_file_anat,
montage_csf_gm_wm, 'inputspec.underlay')
workflow.connect(csf_overlay, out_file_csf,
montage_csf_gm_wm, 'inputspec.overlay_csf')
workflow.connect(wm_overlay, out_file_wm,
montage_csf_gm_wm, 'inputspec.overlay_wm')
workflow.connect(gm_overlay, out_file_gm,
montage_csf_gm_wm, 'inputspec.overlay_gm')
strat.update_resource_pool({
'qc___csf_gm_wm_a': (montage_csf_gm_wm, 'outputspec.axial_png'),
'qc___csf_gm_wm_s': (montage_csf_gm_wm, 'outputspec.sagittal_png')
})
if not 2 in qc_montage_id_a:
qc_montage_id_a[2] = 'csf_gm_wm_a'
qc_montage_id_s[2] = 'csf_gm_wm_s'
if 'functional_preprocessed' in strat:
preproc, out_file_preproc = strat['functional_to_standard']
mean_preproc, out_file_mean_preproc = strat['mean_functional_to_standard']
# make QC Carpet plot
carpet_seg = create_qc_carpet('carpet_seg_%d' % num_strat,
'carpet_seg')
workflow.connect(
preproc, out_file_preproc,
carpet_seg, 'inputspec.functional_to_standard'
)
workflow.connect(
mean_preproc, out_file_mean_preproc,
carpet_seg, 'inputspec.mean_functional_to_standard'
)
workflow.connect(
c.PRIORS_GRAY, 'local_path',
carpet_seg, 'inputspec.anatomical_gm_mask'
)
workflow.connect(
c.PRIORS_WHITE, 'local_path',
carpet_seg, 'inputspec.anatomical_wm_mask'
)
workflow.connect(
c.PRIORS_CSF, 'local_path',
carpet_seg, 'inputspec.anatomical_csf_mask'
)
strat.update_resource_pool({
'qc___carpet': (carpet_seg, 'outputspec.carpet_plot'),
})
if not 9 in qc_plot_id:
qc_plot_id[9] = 'carpet'
if 'functional_preprocessed' in strat:
# make QC montage for Mean Functional in T1 with T1 edge
anat, out_file = strat['anatomical_brain']
m_f_a, out_file_mfa = strat['mean_functional_in_anat']
anat_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='anat_edge_%d' % num_strat)
montage_anat = create_montage(
'montage_anat_%d' % num_strat, 'red', 't1_edge_on_mean_func_in_t1')
workflow.connect(anat, out_file, anat_edge, 'in_file')
workflow.connect(anat_edge, 'out_file',
montage_anat, 'inputspec.overlay')
workflow.connect(m_f_a, out_file_mfa,
montage_anat, 'inputspec.underlay')
strat.update_resource_pool({
'qc___mean_func_with_t1_edge_a': (montage_anat, 'outputspec.axial_png'),
'qc___mean_func_with_t1_edge_s': (montage_anat, 'outputspec.sagittal_png')
})
if not 4 in qc_montage_id_a:
qc_montage_id_a[4] = 'mean_func_with_t1_edge_a'
qc_montage_id_s[4] = 'mean_func_with_t1_edge_s'
# make QC montage for Mean Functional in MNI with MNI edge
m_f_i, out_file = strat['mean_functional_to_standard']
montage_mfi = create_montage(
'montage_mfi_%d' % num_strat, 'red', 'MNI_edge_on_mean_func_mni')
workflow.connect(m_f_i, out_file, montage_mfi,
'inputspec.underlay')
montage_mfi.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'
)
)
strat.update_resource_pool({'qc___mean_func_with_mni_edge_a': (montage_mfi, 'outputspec.axial_png'),
'qc___mean_func_with_mni_edge_s': (montage_mfi, 'outputspec.sagittal_png')})
if not 5 in qc_montage_id_a:
qc_montage_id_a[5] = 'mean_func_with_mni_edge_a'
qc_montage_id_s[5] = 'mean_func_with_mni_edge_s'
# Link all the derivatives to the QC pages
idx = 7
rp = strat.get_resource_pool()
for key in sorted(rp.keys()):
# qc_outputs is from the outputs CSV
if key in qc_outputs:
qa_montages(workflow, c, strat, num_strat,
qc_montage_id_a, qc_montage_id_s, qc_hist_id,
key, idx)
idx += 1
return qc_montage_id_a, qc_montage_id_s, qc_hist_id, qc_plot_id