def ApplyTransform_pipeline(self, **kwargs):
pipeline = self.new_pipeline(
name='applytransform',
desc=('Apply transformation the the 4D PET timeseries'),
citations=[],
**kwargs)
merge_trans = pipeline.add('merge_transforms',
Merge(2),
inputs={
'in1': ('warp_file', nifti_gz_format),
'in2':
('affine_mat', text_matrix_format)
})
pipeline.add(
'ApplyTransform',
ApplyTransforms(reference_image=self.parameter('trans_template'),
interpolation='Linear',
input_image_type=3),
inputs={
'input_image': ('pet_volumes', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
outputs={'registered_volumes': ('output_image', nifti_gz_format)})
return pipeline
def cet_T1(self, **name_maps):
pipeline = self.new_pipeline(
name='CET_T1',
name_maps=name_maps,
desc=("Construct cerebellum mask using SUIT template"),
citations=[fsl_cite])
# FIXME: Should convert to inputs
nl = self._lookup_nl_tfm_inv_name('MNI')
linear = self._lookup_l_tfm_to_name('MNI')
# Initially use MNI space to warp SUIT into T1 and threshold to mask
merge_trans = pipeline.add('merge_transforms',
Merge(2),
inputs={
'in2': (nl, nifti_gz_format),
'in1': (linear, nifti_gz_format)
})
apply_trans = pipeline.add('ApplyTransform',
ants.resampling.ApplyTransforms(
interpolation='NearestNeighbor',
input_image_type=3,
invert_transform_flags=[True, False]),
inputs={
'reference_image':
('betted_T1', nifti_gz_format),
'input_image':
('suit_mask', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=120)
pipeline.add('maths2',
fsl.utils.ImageMaths(suffix='_optiBET_cerebellum',
op_string='-mas'),
inputs={
'in_file': ('betted_T1', nifti_gz_format),
'in_file2': (apply_trans, 'output_image')
},
outputs={
'cetted_T1': ('out_file', nifti_gz_format),
'cetted_T1_mask': ('output_image', nifti_gz_format)
},
requirements=[fsl_req.v('5.0.8')],
mem_gb=16,
wall_time=5)
return pipeline
def combined_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='combined',
desc=("A dummy pipeline used to test MultiMultiStudy class"),
citations=[],
name_maps=name_maps)
merge = pipeline.add("merge", Merge(3))
math = pipeline.add("math", TestMath())
math.inputs.op = self.parameter('combined_op')
math.inputs.as_file = True
# Connect inputs
pipeline.connect_input('ss1_z', merge, 'in1')
pipeline.connect_input('full_e', merge, 'in2')
pipeline.connect_input('partial_ss2_z', merge, 'in3')
# Connect nodes
pipeline.connect(merge, 'out', math, 'x')
# Connect outputs
pipeline.connect_output('g', math, 'z')
return pipeline
def cet_T2s(self, **options):
pipeline = self.new_pipeline(
name='CET_T2s',
desc=("Construct cerebellum mask using SUIT template"),
default_options={
'SUIT_mask': self._lookup_template_mask_path('SUIT')
},
citations=[fsl_cite],
options=options)
# Initially use MNI space to warp SUIT mask into T2s space
merge_trans = pipeline.add(
'merge_transforms',
Merge(3),
inputs={
'in3': (self._lookup_nl_tfm_inv_name('SUIT'), nifti_gz_format),
'in2': (self._lookup_l_tfm_to_name('SUIT'), nifti_gz_format),
'in1': ('T2s_to_T1_mat', text_matrix_format)
})
apply_trans = pipeline.add(
'ApplyTransform',
ants.resampling.ApplyTransforms(
interpolation='NearestNeighbor',
input_image_type=3,
invert_transform_flags=[True, True, False],
input_image=pipeline.option('SUIT_mask')),
inputs={
'transforms': (merge_trans, 'out'),
'reference_image': ('betted_T2s', nifti_gz_format)
},
outputs={'cetted_T2s_mask': ('output_image', nifti_gz_format)},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=120)
# Combine masks
maths1 = pipeline.add('combine_masks',
fsl.utils.ImageMaths(suffix='_optiBET_masks',
op_string='-mas',
output_type='NIFTI_GZ'),
inputs={
'in_file':
('betted_T2s_mask', nifti_gz_format),
'in_file2': (apply_trans, 'output_image')
},
requirements=[fsl_req.v('5.0.8')],
mem_gb=16,
wall_time=5)
# Mask out t2s image
pipeline.add('mask_t2s',
fsl.utils.ImageMaths(suffix='_optiBET_cerebellum',
op_string='-mas',
output_type='NIFTI_GZ'),
inputs={
'in_file': ('betted_T2s', nifti_gz_format),
'in_file2': (maths1, 'output_image')
},
outputs={'cetted_T2s': ('out_file', nifti_gz_format)},
requirements=[fsl_req.v('5.0.8')],
mem_gb=16,
wall_time=5)
pipeline.add(
'mask_t2s_last_echo',
fsl.utils.ImageMaths(suffix='_optiBET_cerebellum',
op_string='-mas',
output_type='NIFTI_GZ'),
inputs={
'in_file': ('betted_T2s_last_echo', nifti_gz_format),
'in_file2': (maths1, 'output_image')
},
outputs={'cetted_T2s_last_echo': ('out_file', nifti_gz_format)},
requirements=[fsl_req.v('5.0.8')],
mem_gb=16,
wall_time=5)
return pipeline
def cv_pipeline(self, **name_maps):
pipeline = self.new_pipeline(name='cv_pipeline',
name_maps=name_maps,
desc="Compute Composite Vein Image",
citations=[fsl_cite, matlab_cite])
# Interpolate priors and atlas
merge_trans = pipeline.add('merge_transforms',
Merge(3),
inputs={
'in1':
('coreg_ants_mat', text_matrix_format),
'in2': ('coreg_to_tmpl_ants_mat',
text_matrix_format),
'in3': ('coreg_to_tmpl_ants_warp',
nifti_gz_format)
})
apply_trans_q = pipeline.add(
'ApplyTransform_Q_Prior',
ants.resampling.ApplyTransforms(
interpolation='Linear',
input_image_type=3,
invert_transform_flags=[True, True, False]),
inputs={
'input_image': ('mni_template_qsm_prior', nifti_gz_format),
'reference_image': ('qsm', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=30)
apply_trans_s = pipeline.add(
'ApplyTransform_S_Prior',
ants.resampling.ApplyTransforms(
interpolation='Linear',
input_image_type=3,
invert_transform_flags=[True, True, False]),
inputs={
'input_image': ('mni_template_swi_prior', nifti_gz_format),
'reference_image': ('qsm', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=30)
apply_trans_a = pipeline.add(
'ApplyTransform_A_Prior',
ants.resampling.ApplyTransforms(
interpolation='Linear',
input_image_type=3,
invert_transform_flags=[True, True, False],
),
inputs={
'reference_image': ('qsm', nifti_gz_format),
'input_image': ('mni_template_atlas_prior', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=30)
apply_trans_v = pipeline.add(
'ApplyTransform_V_Atlas',
ants.resampling.ApplyTransforms(
interpolation='Linear',
input_image_type=3,
invert_transform_flags=[True, True, False]),
inputs={
'input_image': ('mni_template_vein_atlas', nifti_gz_format),
'reference_image': ('qsm', nifti_gz_format),
'transforms': (merge_trans, 'out')
},
requirements=[ants_req.v('1.9')],
mem_gb=16,
wall_time=30)
# Run CV code
pipeline.add(
'cv_image',
interface=CompositeVeinImage(),
inputs={
'mask': ('brain_mask', nifti_format),
'qsm': ('qsm', nifti_format),
'swi': ('swi', nifti_format),
'q_prior': (apply_trans_q, 'output_image'),
's_prior': (apply_trans_s, 'output_image'),
'a_prior': (apply_trans_a, 'output_image'),
'vein_atlas': (apply_trans_v, 'output_image')
},
outputs={'composite_vein_image': ('out_file', nifti_format)},
requirements=[matlab_req.v('R2015a')],
wall_time=300,
mem_gb=24)
return pipeline