def dicom_convert_ppl(name='t1_dicom_cvt', crop_t1=True):
inputnode = pe.Node(utility.IdentityInterface(fields=[
'dicom_files', 'out_file_format', 'voxel_order', 'meta_force_add'
]),
run_without_submitting=True,
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(fields=['t1_nifti']),
run_without_submitting=True,
name='outputspec')
n_convert_t1_dicom = pe.Node(np_dcmstack.DCMStackAnatomical(),
name='convert_t1_dicom')
n_autobox_t1 = pe.Node(afni.Autobox(), name='autobox_t1')
n_crop_t1 = pe.Node(nipy.Crop(out_file='%s_crop.nii', outputtype='NIFTI'),
name='crop_t1')
w = pe.Workflow(name=name)
def zmax2keep(z):
return max(0, z - 174)
wmax = wrap(max)
w.connect([
(inputnode,
n_convert_t1_dicom, [('dicom_files', 'dicom_files'),
('out_file_format', 'out_file_format'),
('voxel_order', 'voxel_order'),
('meta_force_add', 'meta_force_add')]),
])
if crop_t1:
w.connect([
(n_convert_t1_dicom, n_autobox_t1, [('nifti_file', 'in_file')]),
(n_convert_t1_dicom, n_crop_t1, [('nifti_file', 'in_file')]),
(n_autobox_t1, n_crop_t1, [(('x_min', wmax, [0]), 'x_min'),
('x_max', 'x_max'),
(('y_min', wmax, [0]), 'y_min'),
('y_max', 'y_max'), ('z_max', 'z_max'),
(('z_max', zmax2keep), 'z_min')]),
(n_autobox_t1, outputnode, [('out_file', 't1_nifti')]),
])
else:
w.connect(n_convert_t1_dicom, 'nifti_file', outputnode, 't1_nifti'),
return w
def run(options):
# fix!
out_dir = os.path.join('option', '1')
err_dir = os.path.join('option', '2')
data_dir = os.path.join('option', '3')
work_dir = os.path.join('something', 'else')
# Workflow
merica_wf = pe.Workflow('merica_wf')
merica_wf.base_dir = work_dir
inputspec = pe.Node(util.IdentityInterface(fields=options.keys()),
name='inputspec')
# Node: subject_iterable
run_iterable = pe.Node(util.IdentityInterface(fields=['run'],
mandatory_inputs=True),
name='run_iterable')
run_iterable.iterables = ('run', runs)
info = dict(mri_files=[['run']])
# Create a datasource node to get the mri files
datasource = pe.Node(nio.DataGrabber(infields=['run'],
outfields=info.keys()),
name='datasource')
datasource.inputs.template = '*'
datasource.inputs.base_directory = abspath(data_dir)
datasource.inputs.field_template = dict(mri_files='%s/func/*.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasource.inputs.ignore_exception = False
datasource.inputs.raise_on_empty = True
meica_wf.connect(run_iterable, 'run', datasource, 'run')
# Create a Function node to rename output files
getsubs = pe.Node(util.Function(input_names=['run', 'mri_files'],
output_names=['subs'],
function=get_subs),
name='getsubs')
getsubs.inputs.ignore_exception = False
meica_wf.connect(run_iterable, 'run', getsubs, 'run')
meica_wf.connect(datasource, 'mri_files', getsubs, 'mri_files')
get_cm = pe.Node(util.Function(input_names=['fname'],
output_names=['x', 'y', 'z'],
function=find_CM),
name='get_cm')
get_obliquity = pe.Node(util.Function(input_names=['fname'],
output_names=['angmerit'],
function=check_obliquity),
name='get_cm')
if get_obliquity.is_oblique == True:
deoblique = pe.Node(afni.Warp(deoblique=True)
name='deoblique')
merica_wf.connect(upstream, 't1', deoblique, 'in_file')
warpspeed = pe.Node(afni.Warp(args='-card2oblique -newgrid 1.0'))
if skull-stripped == False:
unifeyes = pe.Node(afni.Unifize()
name='unifeyes')
if get_obliquity.is_oblique == True:
merica_wf.connect(deoblique, 'out_file', unifeyes, 'in_file')
else:
merica_wf.connect(upstream, 't1', unifeyes, 'in_file')
skullstrip = pe.Node(afni.SkullStrip(args='-shrink_fac_bot_lim 0.3 -orig_vol')
name='skullstrip')
autobots = pe.Node(afni.Autobox()
name='autobots')
merica_wf.connect(skullstrip, 'out_file', autobots, 'in_file')
# Moving on to functional preprocessing, be back later!
if despike == True:
despike = pe.Node(afni.Despike()
name='despike')
if skull-stripped == False:
merica_wf.connect(autobots, 'out_file', despike, 'in_file')
else:
merica_wf.connect(upstream, 't1', despike, 'in_file')
meica_wf.connect(run_iterable, 'run', get_cm, 'fname')
meica_wf.connect(run_iterable, 'run', get_cm, 'fname')
def t1_pipeline(name='t1_preproc'):
inputnode = pe.Node(utility.IdentityInterface(fields=['t1_dicom_dir']),
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(
fields=['mask', 'corrected_t1', 'corrected_t1_brain']),
name='outputspec')
n_t1_dicom_files = pe.Node(nio.DataGrabber(sort_filelist=True, ),
name='t1_dicom_files')
n_to3d_t1 = pe.Node(afni.To3D(filetype='anat',
environ=dict(AFNI_DICOM_RESCALE='YES')),
name='to3d_t1')
n_reorient_t1 = pe.Node(afni.Resample(orientation='RPI'),
name='reorient_t1')
n_autobox_t1 = pe.Node(afni.Autobox(padding=5), name='autobox_t1')
n_zcut_t1 = pe.Node(afni.ZCutUp(outputtype='NIFTI'), name='zcut_t1')
n_newsegment_t1 = pe.Node(spm.NewSegment(
write_deformation_fields=[True, True],
channel_info=(0.0001, 60, (True, True))),
name='newsegment_t1')
n_seg2mask = pe.Node(fsl.MultiImageMaths(
output_type='NIFTI',
op_string=' -add %s -add %s -thr 0.8 -bin -eroF -dilF -dilF'),
name='seg2mask')
n_mask_brain = pe.Node(interface=fsl.ImageMaths(op_string='-mul',
suffix='_brain',
output_type='NIFTI'),
name='mask_brain')
w = pe.Workflow(name=name)
def zmax2keep(z):
return '%d %d' % (max(0, z - 174), z)
w.connect([
(inputnode, n_t1_dicom_files, [('t1_dicom_dir', 'base_directory')]),
(n_t1_dicom_files, n_to3d_t1,
[(('outfiles', sort_t1_files), 'in_files'),
(('outfiles', t1_filename, 'nii.gz'), 'out_file')]),
(n_to3d_t1, n_reorient_t1, [('out_file', 'in_file')]),
(n_reorient_t1, n_autobox_t1, [('out_file', 'in_file')]),
(n_reorient_t1, n_zcut_t1, [('out_file', 'in_file')]),
(n_autobox_t1, n_zcut_t1, [(('z_max', zmax2keep), 'keep')]),
(n_zcut_t1, n_newsegment_t1, [('out_file', 'channel_files')]),
(n_newsegment_t1, n_seg2mask, [(('native_class_images', getitem_rec, 0,
0), 'in_file'),
(('native_class_images', getitem_rec,
slice(1, 3), 0), 'operand_files')]),
(n_zcut_t1, n_seg2mask, [(('out_file', fname_presuffix_basename, '',
'_mask', '.'), 'out_file')]),
(n_newsegment_t1, n_mask_brain, [('bias_corrected_images', 'in_file')
]),
(n_seg2mask, n_mask_brain, [('out_file', 'in_file2')]),
(n_seg2mask, outputnode, [('out_file', 'mask')]),
(n_newsegment_t1, outputnode, [('bias_corrected_images',
'corrected_t1')])
])
return w
def t1_pipeline(name='t1_preproc'):
inputnode = pe.Node(utility.IdentityInterface(fields=['t1_file']),
run_without_submitting=True,
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(
fields=['mask', 'corrected_t1', 'corrected_t1_brain']),
run_without_submitting=True,
name='outputspec')
spm_path = spm.Info.version()['path']
tpm = os.path.join(spm_path, 'toolbox', 'Seg', 'TPM.nii')
n_newsegment_t1 = pe.Node(spm.NewSegment(
write_deformation_fields=[False, True],
channel_info=(0.0001, 60, (True, True)),
tissues=[
((tpm, 1), 2, (True, True), (True, True)),
((tpm, 2), 2, (True, True), (True, True)),
((tpm, 3), 2, (True, False), (True, True)),
((tpm, 4), 3, (True, False), (False, False)),
((tpm, 5), 4, (True, False), (False, False)),
((tpm, 6), 2, (False, False), (False, False)),
]),
name='newsegment_t1')
n_seg2mask = pe.Node(fsl.MultiImageMaths(
output_type='NIFTI',
output_datatype='char',
op_string=' -add %s -add %s -thr 0.8 -bin -eroF -dilF -dilF'),
name='seg2mask')
n_autobox_mask = pe.Node(afni.Autobox(padding=3, out_name='%s_crop'),
name='autobox_mask')
n_merge_crop = pe.Node(utility.Merge(3), name='merge_crop')
n_crop_all = pe.MapNode(nipy.Crop(out_file='%s_crop.nii.gz',
outputtype='NIFTI_GZ'),
iterfield=['in_file'],
name='crop_all')
w = pe.Workflow(name=name)
w.connect([
(inputnode, n_newsegment_t1, [('t1_file', 'channel_files')]),
(n_newsegment_t1, n_seg2mask, [(('native_class_images', getitem_rec, 0,
0), 'in_file'),
(('native_class_images', getitem_rec,
slice(1, 3), 0), 'operand_files')]),
(inputnode, n_seg2mask, [(('t1_file', fname_presuffix_basename, '',
'_mask', '.'), 'out_file')]),
(n_seg2mask, n_autobox_mask, [('out_file', 'in_file')]),
(n_newsegment_t1, n_merge_crop,
[('bias_corrected_images', 'in1'),
(('native_class_images', utility.select, [0, 1, 2]), 'in2')]),
(n_seg2mask, n_merge_crop, [('out_file', 'in3')]),
(n_merge_crop, n_crop_all, [(('out', utility.flatten), 'in_file')]),
(n_autobox_mask, n_crop_all, [
('x_min', 'x_min'),
('x_max', 'x_max'),
('y_min', 'y_min'),
('y_max', 'y_max'),
('z_min', 'z_min'),
('z_max', 'z_max'),
]), (n_seg2mask, outputnode, [('out_file', 'mask')]),
(n_newsegment_t1, outputnode, [('bias_corrected_images',
'corrected_t1')])
])
return w
def t1_freesurfer_pipeline(name='t1_preproc', use_T2=False):
inputnode = pe.Node(utility.IdentityInterface(
fields=['t1_files', 't2_file', 'subject_id']),
run_without_submitting=True,
name='inputspec')
n_freesurfer = pe.Node(
interface=freesurfer.ReconAll(directive='all', use_T2=use_T2),
name='freesurfer',
)
n_fs_seg2mask = pe.Node(utility.Function(
input_names=['parc_file', 'out_file'],
output_names=['out_file'],
function=fs_seg2mask),
name='fs_seg2mask')
n_fs_seg2mask.inputs.out_file = 'mask.nii'
n_autobox_mask_fs = pe.Node(afni.Autobox(padding=15,
out_file='%s_crop.nii'),
name='autobox_mask_fs')
n_compute_pvmaps = pe.Node(freesurfer.utils.ComputeVolumeFractions(
gm_file='pve.nii.gz',
niigz=True,
),
name='compute_pvmaps')
n_crop_t1 = pe.Node(nipy.Crop(out_file='%s_crop.nii.gz',
outputtype='NIFTI_GZ'),
name='crop_t1')
n_crop_brain = pe.Node(nipy.Crop(out_file='%s_crop.nii.gz',
outputtype='NIFTI_GZ'),
name='crop_brain')
n_t1_nii = pe.Node(freesurfer.MRIConvert(out_type='niigz'), name='t1_nii')
n_dilate_mask = pe.Node(fsl.DilateImage(operation='mean',
kernel_shape='3D'),
name='dilate_mask')
n_reg_crop = pe.Node(freesurfer.Tkregister(reg_file='reg_crop.dat',
freeview='freeview.mat',
fsl_reg_out='fsl_reg_out.mat',
lta_out='lta.mat',
xfm_out='xfm.mat',
reg_header=True),
name='reg_crop')
w = pe.Workflow(name=name)
if (use_T2):
w.connect([(inputnode, n_freesurfer, [
('t2_file', 'T2_file'),
])])
w.connect([
(inputnode, n_freesurfer, [('t1_files', 'T1_files'),
('subject_id', 'subject_id')]),
(n_freesurfer, n_fs_seg2mask, [(('aparc_aseg', utility.select, 1),
'parc_file')]),
(n_fs_seg2mask, n_autobox_mask_fs, [('out_file', 'in_file')]),
(n_autobox_mask_fs, n_dilate_mask, [('out_file', 'in_file')]),
(n_autobox_mask_fs, n_crop_t1, [('%s_min' % c, ) * 2 for c in 'xyz'] +
[(('%s_max' % c, wrap(operator.__add__), [1]), '%s_max' % c)
for c in 'xyz']), # add 1 for boundary inclusive
(n_freesurfer, n_crop_t1, [('T1', 'in_file')]),
(n_autobox_mask_fs, n_crop_brain, [('%s_min' % c, ) * 2
for c in 'xyz'] +
[(('%s_max' % c, wrap(operator.__add__), [1]), '%s_max' % c)
for c in 'xyz']), # add 1 for boundary inclusive
(n_freesurfer, n_crop_brain, [('norm', 'in_file')]),
(n_freesurfer, n_t1_nii, [('T1', 'in_file')]),
(n_freesurfer, n_reg_crop, [('norm', 'target'), ('subject_id', ) * 2,
('subjects_dir', ) * 2]),
(n_autobox_mask_fs, n_reg_crop, [('out_file', 'mov')]),
(n_reg_crop, n_compute_pvmaps, [('reg_file', ) * 2]),
(n_autobox_mask_fs, n_compute_pvmaps, [('out_file', 'in_file')]),
(n_freesurfer, n_compute_pvmaps, [('subjects_dir', ) * 2])
])
return w
def t1_vbm_pipeline(name='t1_preproc'):
inputnode = pe.Node(utility.IdentityInterface(fields=['t1_file']),
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(
fields=['mask', 'corrected_t1', 'corrected_t1_brain']),
name='outputspec')
spm_path = spm.Info.version()['path']
tpm = os.path.join(spm_path, 'toolbox', 'Seg', 'TPM.nii')
dartel_tpl = os.path.join(spm_path, 'toolbox', 'vbm8',
'Template_1_IXI550_MNI152.nii')
n_vbm_segment = pe.Node(spm.VBMSegment(tissues=tpm,
dartel_template=dartel_tpl,
bias_corrected_native=True,
gm_native=True,
gm_dartel=1,
wm_native=True,
wm_dartel=1,
csf_native=True,
pve_label_native=True,
deformation_field=(True, False)),
name='vbm_segment')
n_seg2mask = pe.Node(fsl.MultiImageMaths(
output_type='NIFTI',
output_datatype='char',
op_string=' -add %s -add %s -thr 0.8 -bin -eroF -dilF -dilF'),
name='seg2mask')
n_seg2mask = pe.Node(utility.Function(
input_names=['tissues_files', 'out_file'],
output_names=['out_file'],
function=seg2mask),
name='seg2mask')
n_autobox_mask = pe.Node(afni.Autobox(padding=3, out_file='%s_crop'),
name='autobox_mask')
n_merge_crop = pe.Node(utility.Merge(3), name='merge_crop')
n_crop_all = pe.MapNode(nipy.Crop(out_file='%s_crop.nii.gz',
outputtype='NIFTI_GZ'),
iterfield=['in_file'],
name='crop_all')
w = pe.Workflow(name=name)
w.connect([
(inputnode, n_vbm_segment, [('t1_file', 'in_files')]),
(n_vbm_segment, n_seg2mask, [(('native_class_images', utility.flatten),
'tissues_files')]),
# (n_vbm_segment, n_seg2mask, [
# (('native_class_images',getitem_rec,0,0), 'in_file'),
# (('native_class_images',getitem_rec,slice(1,3),0),'operand_files')]),
(inputnode, n_seg2mask, [(('t1_file', fname_presuffix_basename, '',
'_mask', '.'), 'out_file')]),
(n_seg2mask, n_autobox_mask, [('out_file', 'in_file')]),
(n_vbm_segment, n_merge_crop, [('bias_corrected_images', 'in1'),
(('native_class_images', utility.select,
[0, 1, 2]), 'in2')]),
(n_seg2mask, n_merge_crop, [('out_file', 'in3')]),
(n_merge_crop, n_crop_all, [(('out', utility.flatten), 'in_file')]),
(n_autobox_mask, n_crop_all, [
('x_min', 'x_min'),
('x_max', 'x_max'),
('y_min', 'y_min'),
('y_max', 'y_max'),
('z_min', 'z_min'),
('z_max', 'z_max'),
]),
(n_seg2mask, outputnode, [('out_file', 'mask')]),
(n_vbm_segment, outputnode, [('bias_corrected_images', 'corrected_t1')
])
])
return w