class TwixConverter(Converter):
input = 'in_file'
output = 'out_file'
output_aux_files = {'ref': 'ref_file', 'json': 'hdr_file'}
requirements = [matlab_req.v('R2018a')]
interface = TwixReader()
def test_unwrap(self):
tmp_dir = tempfile.mkdtemp()
in_file = op.join(tmp_dir, 'in_file.nii.gz')
with open(in_file, 'w') as f:
f.write('test')
out_file = op.join(tmp_dir, 'out_file.nii.gz')
unwrap = TEST_ENV.make_node(UnwrapPhase(), name='unwrap',
requirements=[matlab_req.v('r2018a'),
sti_req.v(2.2)])
unwrap.inputs.in_file = in_file
unwrap.inputs.voxelsize = [2.0, 2.0, 2.0]
# unwrap.inputs.out_file = out_file
unwrap.run()
self.assertTrue(op.exists(out_file))
def shmrf_pipeline(self, **name_maps):
pipeline = self.new_pipeline(name='shmrf_pipeline',
name_maps=name_maps,
desc="Compute Vein Mask using ShMRF",
citations=[fsl_cite, matlab_cite])
# Run ShMRF code
pipeline.add('shmrf',
ShMRF(),
inputs={
'in_file': ('composite_vein_image', nifti_format),
'mask': ('brain_mask', nifti_format)
},
outputs={'vein_mask': ('out_file', nifti_format)},
requirements=[matlab_req.v('R2015a')],
wall_time=30,
mem_gb=16)
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
def qsm_pipeline(self, **name_maps):
"""
Process dual echo data for QSM (TE=[7.38, 22.14])
NB: Default values come from the STI-Suite
"""
pipeline = self.new_pipeline(
name='qsm_pipeline',
name_maps=name_maps,
desc="Resolve QSM from t2star coils",
citations=[sti_cites, fsl_cite, matlab_cite])
erosion = pipeline.add(
'mask_erosion',
fsl.ErodeImage(kernel_shape='sphere',
kernel_size=self.parameter('qsm_erosion_size'),
output_type='NIFTI'),
inputs={'in_file': ('brain_mask', nifti_gz_format)},
requirements=[fsl_req.v('5.0.8')],
wall_time=15,
mem_gb=12)
# If we have multiple echoes we can combine the phase images from
# each channel into a single image. Otherwise for single echo sequences
# we need to perform QSM on each coil separately and then combine
# afterwards.
if self.branch('qsm_dual_echo'):
# Combine channels to produce phase and magnitude images
channel_combine = pipeline.add(
'channel_combine',
HIPCombineChannels(),
inputs={
'magnitudes_dir': ('mag_channels', multi_nifti_gz_format),
'phases_dir': ('phase_channels', multi_nifti_gz_format)
})
# Unwrap phase using Laplacian unwrapping
unwrap = pipeline.add(
'unwrap',
UnwrapPhase(padsize=self.parameter('qsm_padding')),
inputs={
'voxelsize': ('voxel_sizes', float),
'in_file': (channel_combine, 'phase')
},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)])
# Remove background noise
vsharp = pipeline.add(
"vsharp",
VSharp(mask_manip="imerode({}>0, ball(5))"),
inputs={
'voxelsize': ('voxel_sizes', float),
'in_file': (unwrap, 'out_file'),
'mask': (erosion, 'out_file')
},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)])
# Run QSM iLSQR
pipeline.add('qsmrecon',
QSMiLSQR(mask_manip="{}>0",
padsize=self.parameter('qsm_padding')),
inputs={
'voxelsize': ('voxel_sizes', float),
'te': ('echo_times', float),
'B0': ('main_field_strength', float),
'H': ('main_field_orient', float),
'in_file': (vsharp, 'out_file'),
'mask': (vsharp, 'new_mask')
},
outputs={'qsm': ('qsm', nifti_format)},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)])
else:
# Dialate eroded mask
dialate = pipeline.add(
'dialate',
DialateMask(dialation=self.parameter('qsm_mask_dialation')),
inputs={'in_file': (erosion, 'out_file')},
requirements=[matlab_req.v('r2017a')])
# List files for the phases of separate channel
list_phases = pipeline.add(
'list_phases',
ListDir(sort_key=coil_sort_key,
filter=CoilEchoFilter(self.parameter('qsm_echo'))),
inputs={
'directory': ('phase_channels', multi_nifti_gz_format)
})
# List files for the phases of separate channel
list_mags = pipeline.add(
'list_mags',
ListDir(sort_key=coil_sort_key,
filter=CoilEchoFilter(self.parameter('qsm_echo'))),
inputs={'directory': ('mag_channels', multi_nifti_gz_format)})
# Generate coil specific masks
mask_coils = pipeline.add(
'mask_coils',
MaskCoils(dialation=self.parameter('qsm_mask_dialation')),
inputs={
'masks': (list_mags, 'files'),
'whole_brain_mask': (dialate, 'out_file')
},
requirements=[matlab_req.v('r2017a')])
# Unwrap phase
unwrap = pipeline.add(
'unwrap',
BatchUnwrapPhase(padsize=self.parameter('qsm_padding')),
inputs={
'voxelsize': ('voxel_sizes', float),
'in_file': (list_phases, 'files')
},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)])
# Background phase removal
vsharp = pipeline.add(
"vsharp",
BatchVSharp(mask_manip='{}>0'),
inputs={
'voxelsize': ('voxel_sizes', float),
'mask': (mask_coils, 'out_files'),
'in_file': (unwrap, 'out_file')
},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)])
first_echo_time = pipeline.add(
'first_echo',
Select(index=0),
inputs={'inlist': ('echo_times', float)})
# Perform channel-wise QSM
coil_qsm = pipeline.add(
'coil_qsmrecon',
BatchQSMiLSQR(mask_manip="{}>0",
padsize=self.parameter('qsm_padding')),
inputs={
'voxelsize': ('voxel_sizes', float),
'B0': ('main_field_strength', float),
'H': ('main_field_orient', float),
'in_file': (vsharp, 'out_file'),
'mask': (vsharp, 'new_mask'),
'te': (first_echo_time, 'out')
},
requirements=[matlab_req.v('r2017a'),
sti_req.v(2.2)],
wall_time=45) # FIXME: Should be dependent on number of coils
# Combine channel QSM by taking the median coil value
pipeline.add('combine_qsm',
MedianInMasks(),
inputs={
'channels': (coil_qsm, 'out_file'),
'channel_masks': (vsharp, 'new_mask'),
'whole_brain_mask': (dialate, 'out_file')
},
outputs={'qsm': ('out_file', nifti_format)},
requirements=[matlab_req.v('r2017a')])
return pipeline