def nifti2dcm_conversion_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='conversion_to_dicom',
desc=("Conversing aligned umap from nifti to dicom format - "
"parallel implementation"),
citations=(),
name_maps=name_maps)
list_niftis = pipeline.add(
'list_niftis',
ListDir(),
inputs={'directory': ('umaps_align2ref', directory_format)})
reorient_niftis = pipeline.add('reorient_niftis',
ReorientUmap(),
inputs={
'niftis': (list_niftis, 'files'),
'umap': ('umap', dicom_format)
},
requirements=[mrtrix_req.v('3.0rc3')])
list_dicoms = pipeline.add(
'list_dicoms',
ListDir(sort_key=dicom_fname_sort_key),
inputs={'directory': ('umap', dicom_format)})
nii2dicom = pipeline.add(
'nii2dicom',
Nii2Dicom(
# extension='Frame', # nii2dicom parameter
),
inputs={'reference_dicom': (list_dicoms, 'files')},
outputs={'in_file': (reorient_niftis, 'reoriented_umaps')},
iterfield=['in_file'],
wall_time=20)
pipeline.add(
'copy2dir',
CopyToDir(extension='Frame'),
inputs={'in_files': (nii2dicom, 'out_file')},
outputs={'umap_aligned_dicoms': ('out_dir', directory_format)})
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
def preprocess_channels_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
'preprocess_channels',
name_maps=name_maps,
desc=("Convert channel signals in complex coords to polar coords "
"and combine"))
if (self.provided('header_image') or
self.branch('reorient_to_std') or
self.parameter('force_channel_flip') is not None):
# Read channel files reorient them into standard space and then
# write back to directory
list_channels = pipeline.add(
'list_channels',
ListDir(),
inputs={
'directory': ('channels', multi_nifti_gz_format)})
if self.parameter('force_channel_flip') is not None:
force_flip = pipeline.add(
'flip_dims',
fsl.SwapDimensions(
new_dims=tuple(self.parameter('force_channel_flip'))),
inputs={
'in_file': (list_channels, 'files')},
iterfield=['in_file'])
geom_dest_file = (force_flip, 'out_file')
else:
geom_dest_file = (list_channels, 'files')
if self.provided('header_image'):
# If header image is provided stomp its geometry over the
# acquired channels
copy_geom = pipeline.add(
'qsm_copy_geometry',
fsl.CopyGeom(
output_type='NIFTI_GZ'),
inputs={
'in_file': ('header_image', nifti_gz_format),
'dest_file': geom_dest_file},
iterfield=(['dest_file']),
requirements=[fsl_req.v('5.0.8')])
reorient_in_file = (copy_geom, 'out_file')
else:
reorient_in_file = geom_dest_file
if self.branch('reorient_to_std'):
reorient = pipeline.add(
'reorient_channel',
fsl.Reorient2Std(
output_type='NIFTI_GZ'),
inputs={
'in_file': reorient_in_file},
iterfield=['in_file'],
requirements=[fsl_req.v('5.0.8')])
copy_to_dir_in_files = (reorient, 'out_file')
else:
copy_to_dir_in_files = reorient_in_file
copy_to_dir = pipeline.add(
'copy_to_dir',
CopyToDir(),
inputs={
'in_files': copy_to_dir_in_files,
'file_names': (list_channels, 'files')})
to_polar_in_dir = (copy_to_dir, 'out_dir')
else:
to_polar_in_dir = ('channels', multi_nifti_gz_format)
pipeline.add(
'to_polar',
ToPolarCoords(
in_fname_re=self.parameter('channel_fname_regex'),
real_label=self.parameter('channel_real_label'),
imaginary_label=self.parameter('channel_imag_label')),
inputs={
'in_dir': to_polar_in_dir},
outputs={
'mag_channels': ('magnitudes_dir', multi_nifti_gz_format),
'phase_channels': ('phases_dir', multi_nifti_gz_format)})
return pipeline