def main(sourcedata, derivatives, tmp_dir, subject, session=None):
brainmask_fn = os.path.join(derivatives, 'freesurfer',
'sub-{}'.format(subject), 'mri',
'brainmask.mgz')
if not os.path.exists(brainmask_fn):
raise Exception(
'Brainmask {} does not exits. Did you run Freesurfer?'.format(
brainmask_fn))
brainmask = image.load_img(brainmask_fn)
manual_outside = get_derivative(derivatives,
type='manual_segmentation',
modality='anat',
subject=subject,
suffix='mask',
description='outside',
space='average',
session=session,
check_exists=False)
manual_outside = image.resample_to_img(manual_outside,
brainmask,
interpolation='nearest')
manual_inside = get_derivative(derivatives,
type='manual_segmentation',
modality='anat',
subject=subject,
suffix='mask',
description='gm',
space='average',
session=session,
check_exists=False)
manual_inside = image.resample_to_img(manual_inside,
brainmask,
interpolation='nearest')
new_brainmask = image.math_img(
'brainmask * (np.ones_like(brainmask) - manual_outside)'
'+ (manual_inside - (brainmask > 0)) ',
brainmask=brainmask,
manual_inside=manual_inside,
manual_outside=manual_outside)
print(new_brainmask.shape)
new_brainmask = nb.freesurfer.MGHImage(new_brainmask.get_data(),
brainmask.affine, brainmask.header)
if not (brainmask.get_data() == new_brainmask.get_data()).all():
print('Brain mask has been altered')
new_brainmask.to_filename(brainmask_fn)
print('writing to {}'.format(brainmask_fn))
else:
print('Brain mask has NOT been altered')
def main(sourcedata,
derivatives,
tmp_dir,
subject,
num_threads=8,
session=None):
if session is None:
session = '.*'
derivatives_layout = BIDSLayout(os.path.join(derivatives, 'averaged_mp2rages'))
inv2 = get_bids_file(derivatives_layout,
subject,
suffix='INV2')
t1w = get_bids_file(derivatives_layout,
subject,
suffix='T1w')
t1map = get_bids_file(derivatives_layout,
subject,
suffix='T1map')
manual_outside = get_derivative(derivatives, type='manual_segmentation',
modality='anat', subject=subject,
suffix='mask', description='outside',
space='average', session=session,
check_exists=False)
manual_inside = get_derivative(derivatives, type='manual_segmentation',
modality='anat', subject=subject,
suffix='mask', description='gm',
space='average', session=session,
check_exists=False)
wf_name = 'mask_wf_{}'.format(subject)
mask_wf = init_masking_wf(name=wf_name, num_threads=num_threads)
mask_wf.base_dir = '/workflow_folders'
mask_wf.inputs.inputnode.inv2 = inv2
mask_wf.inputs.inputnode.t1w = t1w
mask_wf.inputs.inputnode.t1map = t1map
mask_wf.inputs.inputnode.manual_inside = manual_inside
mask_wf.inputs.inputnode.manual_outside = manual_outside
mask_wf.run()
def build_model(image, param1, param2, cycle4, cycle8, facet):
"""
build ilp model for piecewise linear
"""
print("Building Cplex model...")
# initialize model
model = cplex.Cplex()
# set sense
model.objective.set_sense(model.objective.sense.minimize)
# get discrete second derivative
derivative = utils.get_derivative(image)
# build graph
graph = utils.to_graph(image)
# build objective function
vars = get_varibles(image)
model.variables.add(names=vars)
colnames, obj, types = get_obj(derivative, param2)
model.variables.add(obj=obj, types=types, names=colnames)
# add constraints
rows, senses, rhs = get_constraints(image,
derivative,
param1,
cycle4=cycle4,
cycle8=cycle8)
model.linear_constraints.add(lin_expr=rows, senses=senses, rhs=rhs)
# parallel
model.parameters.parallel.set(-1)
model.parameters.threads.set(32)
# register callback
#model.register_callback(cutremoveCallback)
model.register_callback(multicutCallback)
# associate additional data
multicutCallback._graph = graph.copy()
multicutCallback._names = model.variables.get_names()
multicutCallback._facet = facet
#cutremoveCallback._names = model.variables.get_names()
return model
def main(sourcedata, derivatives, subject):
manual_outside_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='outside',
session='anat',
check_exists=True,
space='average')
manual_inside_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='gm',
session='anat',
check_exists=True,
space='average')
manual_wm_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='wm',
session='anat',
check_exists=False,
space='average')
freesurfer_brainmask_auto = op.join(
derivatives, 'freesurfer', 'sub-{subject}', 'mri',
'brainmask.auto.mgz').format(**locals())
freesurfer_brainmask = op.join(derivatives, 'freesurfer', 'sub-{subject}',
'mri', 'brainmask.mgz').format(**locals())
freesurfer_t1w = op.join(derivatives, 'freesurfer', 'sub-{subject}', 'mri',
'T1.mgz').format(**locals())
manual_outside_mask = image.resample_to_img(manual_outside_mask,
freesurfer_brainmask_auto,
interpolation='nearest')
manual_outside_mask = nb.freesurfer.MGHImage(
manual_outside_mask.get_data().astype(np.float32),
affine=manual_outside_mask.affine)
manual_inside_mask = image.resample_to_img(manual_inside_mask,
freesurfer_brainmask_auto,
interpolation='nearest')
manual_inside_mask = nb.freesurfer.MGHImage(
manual_inside_mask.get_data().astype(np.float32),
affine=manual_inside_mask.affine)
if manual_wm_mask:
manual_wm_mask = image.resample_to_img(manual_wm_mask,
freesurfer_brainmask_auto,
interpolation='nearest')
manual_wm_mask = nb.freesurfer.MGHImage(
manual_wm_mask.get_data().astype(np.float32),
affine=manual_wm_mask.affine)
manual_inside_mask = image.math_img(
'(manual_inside_mask + manual_wm_mask) > 0',
manual_inside_mask=manual_inside_mask,
manual_wm_mask=manual_wm_mask)
freesurfer_wm = op.join(derivatives, 'freesurfer', 'sub-{subject}',
'mri', 'wm.mgz').format(**locals())
freesurfer_wm_new = image.math_img(
'freesurfer_wm * (1-manual_inside_mask) '
'* (1 - manual_outside_mask)'
'+ manual_wm_mask * 255',
manual_inside_mask=manual_inside_mask,
manual_wm_mask=manual_wm_mask,
manual_outside_mask=manual_outside_mask,
freesurfer_wm=freesurfer_wm)
# Get rid of any weird small components
freesurfer_wm_new_ = nb.Nifti1Image(freesurfer_wm_new.get_data(),
freesurfer_wm_new.affine)
largest_component = image.largest_connected_component_img(
freesurfer_wm_new_)
largest_component = nb.MGHImage(largest_component.get_data(),
freesurfer_wm_new.affine,
freesurfer_wm_new.header)
freesurfer_wm_new = image.math_img('freesurfer_wm * largest_component',
freesurfer_wm=freesurfer_wm_new,
largest_component=largest_component)
freesurfer_wm_new.to_filename(freesurfer_wm)
new_brainmask = image.math_img(
'(((brain_mask > 0) + inside - outside ) > 0) * t1w',
brain_mask=freesurfer_brainmask_auto,
outside=manual_outside_mask,
t1w=freesurfer_t1w,
inside=manual_inside_mask)
new_brainmask.to_filename(freesurfer_brainmask)
new_brainmask.to_filename(
op.join(derivatives, 'freesurfer', 'sub-{subject}', 'mri',
'brain.finalsurfs.manedit.mgz').format(**locals()))
def main(derivatives,
subject,
session,
task,
acquisition,
run,
workflow_folder='/tmp/workflow_folders'):
os.environ['SUBJECTS_DIR'] = os.path.join(derivatives, 'freesurfer')
preproc_bold = get_derivative(derivatives,
'spynoza',
'func',
subject=subject,
session=session,
suffix='preproc',
acquisition=acquisition,
run=run,
task=task)
registration = get_derivative(derivatives,
'manual_registrations',
'func',
subject=subject,
session=session,
description='spynoza2t1w',
suffix='transform',
extension='lta',
check_exists=False)
wf = pe.Workflow(name='sample_fs', base_dir=workflow_folder)
inputnode = pe.Node(niu.IdentityInterface(
fields=['preproc_bold', 'registration'
'subject']),
name='inputnode')
inputnode.inputs.preproc_bold = preproc_bold
inputnode.inputs.subject = 'sub-{}'.format(subject)
inputnode.inputs.registration = registration
sampler = pe.Node(fs.SampleToSurface(sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
interp_method='trilinear',
cortex_mask=True,
subjects_dir=os.path.join(
derivatives, 'freesurfer'),
override_reg_subj=True,
out_type='gii'),
iterables=('hemi', ['lh', 'rh']),
name='sampler')
wf.connect(inputnode, 'preproc_bold', sampler, 'source_file')
if registration is not None:
wf.connect(inputnode, 'registration', sampler, 'reg_file')
else:
sampler.inputs.reg_header = True
wf.connect(inputnode, 'subject', sampler, 'subject_id')
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'])
wf.connect(sampler, 'out_file', merger, 'in1')
ds = pe.MapNode(DerivativesDataSink(
base_directory=derivatives,
out_path_base='sampled_giis',
),
iterfield=['in_file', 'suffix'],
name='datasink')
ds.inputs.suffix = ['bold.lh', 'bold.rh']
wf.connect(merger, 'out', ds, 'in_file')
wf.connect(inputnode, 'preproc_bold', ds, 'source_file')
wf.run()
def main(sourcedata,
derivatives,
tmp_dir,
subject=None,
session=None,
run=None):
print(subject, session, run)
layout = BIDSLayout(sourcedata)
derivatives_layout = BIDSLayout('/derivatives/spynoza', validate=False)
cortex_l = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
cortex_r = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
mask = derivatives_layout.get(subject=subject,
session=session,
suffix='mask',
return_type='file')
mask = sorted(mask)
assert (len(mask) == 1)
mask = mask[0]
bold = derivatives_layout.get(subject=subject,
session=session,
suffix='preproc',
return_type='file')
bold = sorted(bold)
print('BOLD: {}'.format(bold))
print('MASK: {}'.format(mask))
inputnode = pe.Node(
niu.IdentityInterface(fields=['cortex_l', 'cortex_r', 'bold', 'mask']),
name='inputnode')
inputnode.inputs.cortex_l = cortex_l
inputnode.inputs.cortex_r = cortex_r
inputnode.inputs.bold = bold
inputnode.inputs.mask = mask
get_masks = pe.MapNode(niu.Function(
function=get_brain_regions_cruise,
input_names=['cortex_l', 'cortex_r', 'type'],
output_names=['out']),
iterfield=['type'],
name='get_masks')
get_masks.inputs.type = ['csf', 'wm']
wf = pe.Workflow(name='get_confounds_{}_{}'.format(subject, session),
base_dir='/workflow_folders')
wf.connect(inputnode, 'cortex_l', get_masks, 'cortex_l')
wf.connect(inputnode, 'cortex_r', get_masks, 'cortex_r')
resampler = pe.MapNode(niu.Function(
function=resample_img,
input_names=['input_image', 'ref_image', 'interpolation'],
output_names=['resampled_image'],
),
iterfield=['input_image'],
name='resampler')
wf.connect(inputnode, ('bold', pickfirst), resampler, 'ref_image')
wf.connect(get_masks, 'out', resampler, 'input_image')
compcorr = pe.MapNode(ACompCor(merge_method='union'),
iterfield=['realigned_file'],
name='acompcorr')
wf.connect(resampler, 'resampled_image', compcorr, 'mask_files')
wf.connect(inputnode, 'bold', compcorr, 'realigned_file')
dvars = pe.MapNode(ComputeDVARS(), iterfield=['in_file'], name='dvars')
wf.connect(inputnode, 'mask', dvars, 'in_mask')
wf.connect(inputnode, 'bold', dvars, 'in_file')
add_header = pe.MapNode(AddTSVHeader(columns=["dvars"]),
iterfield=['in_file'],
name="add_header_dvars")
wf.connect(dvars, 'out_std', add_header, 'in_file')
concat = pe.MapNode(GatherConfounds(),
iterfield=['acompcor', 'dvars'],
name="concat")
wf.connect(add_header, 'out_file', concat, 'dvars')
wf.connect(compcorr, 'components_file', concat, 'acompcor')
ds_confounds = pe.MapNode(DerivativesDataSink(out_path_base='spynoza',
suffix='confounds_compcor',
base_directory=derivatives),
iterfield=['in_file', 'source_file'],
name='ds_reg_report')
wf.connect(inputnode, 'bold', ds_confounds, 'source_file')
wf.connect(concat, 'confounds_file', ds_confounds, 'in_file')
wf.run(plugin='MultiProc', plugin_args={'n_procs': 10})
def main(derivatives, subject, session, workflow_folder, n_procs=8):
fn = get_derivative(derivatives,
'tsnr',
'func',
subject=subject,
session=session,
run='*',
acquisition='*',
task='*',
suffix='invtsnr')
os.environ['SUBJECTS_DIR'] = op.join(derivatives, 'freesurfer')
wf = pe.Workflow(name='sample_fs_invtsnt_{}_{}'.format(subject, session),
base_dir=workflow_folder)
input_node = pe.Node(niu.IdentityInterface(fields=['source_file']),
name='input_node')
input_node.inputs.source_file = fn
config_node = pe.Node(
niu.IdentityInterface(fields=['depth', 'hemisphere']),
name='config_node')
config_node.iterables = [('depth', np.arange(1, 7)),
('hemisphere', ['lh', 'rh'])]
def get_surf_name(depth, n_surfs=8):
return 'equi{}.pial'.format(str(float(depth) / (n_surfs - 1)))
sampler = pe.MapNode(fs.SampleToSurface(
subjects_dir=os.path.join(derivatives, 'freesurfer'),
override_reg_subj=True,
reg_header=True,
subject_id='sub-{}'.format(subject),
interp_method='trilinear',
projection_stem='',
out_type='gii'),
iterfield=['source_file'],
name='sampler')
wf.connect(input_node, 'source_file', sampler, 'source_file')
wf.connect(config_node, ('depth', get_surf_name), sampler, 'surface')
wf.connect(config_node, 'hemisphere', sampler, 'hemi')
def get_desc(depth, n_surfs=8):
return 'depth.{:.03f}'.format(float(depth) / (n_surfs - 1))
def get_extra_values(hemi):
return ['hemi-{}'.format(hemi)]
ds = pe.MapNode(DerivativesDataSink(
base_directory=derivatives,
out_path_base='tsnr',
),
iterfield=['in_file', 'source_file'],
name='datasink')
wf.connect(input_node, 'source_file', ds, 'source_file')
wf.connect(sampler, 'out_file', ds, 'in_file')
wf.connect(config_node, ('depth', get_desc), ds, 'desc')
wf.connect(config_node, ('hemisphere', get_extra_values), ds,
'extra_values')
wf.run(plugin='MultiProc', plugin_args={'n_procs': n_procs})
def main(sourcedata, derivatives, tmp_dir, subject=None):
layout = BIDSLayout(sourcedata)
inv2 = get_derivative(derivatives,
'averaged_mp2rages',
'anat',
subject,
'INV2',
session='anat',
space='average')
t1w = get_derivative(derivatives,
'averaged_mp2rages',
'anat',
subject,
'T1w',
session='anat',
space='average')
t1map = get_derivative(derivatives,
'averaged_mp2rages',
'anat',
subject,
'T1map',
session='anat',
space='average')
manual_outside_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='outside',
session='anat',
check_exists=False,
space='average')
manual_gm_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='gm',
session='anat',
check_exists=True,
space='average')
manual_wm_mask = get_derivative(derivatives,
'manual_segmentation',
'anat',
subject,
'mask',
description='wm',
session='anat',
check_exists=False,
space='average')
fmriprep_wm = get_derivative(derivatives,
'fmriprep',
'anat',
subject,
'probseg',
label='WM',
old_fmriprep=False)
fmriprep_gm = get_derivative(derivatives,
'fmriprep',
'anat',
subject,
'probseg',
label='GM',
old_fmriprep=False)
freesurfer_seg = get_derivative(derivatives,
'fmriprep',
'anat',
subject,
'dseg',
description='aseg',
old_fmriprep=False)
name = 'nighres_{}'.format(subject)
wf = init_from_fmriprep_to_nighres_wf(name)
wf.base_dir = tmp_dir
wf.inputs.inputnode.inv2 = inv2
wf.inputs.inputnode.t1w = t1w
wf.inputs.inputnode.t1map = t1map
wf.inputs.inputnode.manual_mask_outside = manual_outside_mask
wf.inputs.inputnode.manual_mask_gm = manual_gm_mask
wf.inputs.inputnode.manual_mask_wm = manual_wm_mask
wf.inputs.inputnode.fmriprep_wm = fmriprep_wm
wf.inputs.inputnode.fmriprep_gm = fmriprep_gm
wf.inputs.inputnode.aseg = freesurfer_seg
wf.run(plugin='MultiProc', plugin_args={'n_procs': 10})
def main(sourcedata,
derivatives,
tmp_dir,
subject=None,
acquisition=None,
session=None,
run=None):
if run is []:
run = '[0-9]+'
if subject in ['04', '01']:
dof = 9
else:
dof = 6
layout = BIDSLayout(sourcedata)
derivatives_layout = BIDSLayout(derivatives)
dtissue_left = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
hemi='left',
space='average',
description='cortex',
session='anat')
dtissue_right = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
hemi='right',
space='average',
description='cortex',
session='anat')
dura = get_dura_mask(derivatives, subject, 'average', 'anat')
get_wm_wf = get_wm_seg_from_nighres(dtissue_left, dtissue_right)
bold = []
bold = layout.get(subject=subject,
session=session,
run=run,
acquisition=acquisition,
suffix='bold',
return_type='file')
print(bold)
epi = []
for b in bold:
fmaps = layout.get_fieldmap(b, return_list=True)
for fmap in fmaps:
if ('type' not in fmap) or (fmap['type'] == 'epi'):
break
epi.append(fmap['epi'])
print('Using {} as epi_op for {}'.format(fmap['epi'], b))
print(epi)
t1w = get_derivative(derivatives,
'masked_mp2rages',
'anat',
subject,
'T1w',
session='anat',
space='average',
description='masked',
extension='nii.gz')
print(t1w, os.path.exists(t1w))
init_matrix = get_derivative(derivatives,
'manual_transformations',
'func',
subject,
'transform',
session=session,
space='average',
extension='mat')
os.environ['SUBJECTS_DIR'] = op.join(derivatives, 'freesurfer')
wf = init_hires_unwarping_wf(
name="unwarp_hires_{}_{}_{}".format(subject, session, acquisition),
method='topup',
bids_layout=layout,
single_warpfield=False,
register_to='last',
init_transform=init_matrix,
linear_registration_parameters='linear_hires.json',
nonlinear_registration_parameters='nonlinear_precise.json',
bold=bold,
epi_op=epi,
t1w_epi=None,
t1w=t1w,
dura_mask=dura,
wm_seg=True,
inv2_epi=None,
dof=dof,
crop_bolds=True,
topup_package='afni',
epi_to_t1_package='fsl',
highpass_filter=True,
within_epi_reg=True,
freesurfer_subject_id='sub-{}'.format(subject),
polish=True,
num_threads_ants=4)
wf.connect(get_wm_wf, 'outputspec.wm_seg', wf.get_node('inputspec'),
'wm_seg')
wf.base_dir = tmp_dir
wf.run(plugin='MultiProc', plugin_args={'n_procs': 8})
