def antsRegistrationTemplateBuildSingleIterationWF(iterationPhasePrefix=''):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
inputspec.interpolationMapping :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name='antsRegistrationTemplateBuildSingleIterationWF_' +
str(iterationPhasePrefix))
inputSpec = pe.Node(interface=util.IdentityInterface(fields=[
'ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping', 'fixed_image'
]),
run_without_submitting=True,
name='inputspec')
## HACK: TODO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: TODO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: TODO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(interface=util.IdentityInterface(
fields=['template', 'transforms_list', 'passive_deformed_templates']),
run_without_submitting=True,
name='outputspec')
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(interface=Registration(),
name='BeginANTS',
iterfield=['moving_image'])
BeginANTS.inputs.dimension = 3
BeginANTS.inputs.output_transform_prefix = str(
iterationPhasePrefix) + '_tfm'
BeginANTS.inputs.transforms = ["Affine", "SyN"]
BeginANTS.inputs.transform_parameters = [[0.9], [0.25, 3.0, 0.0]]
BeginANTS.inputs.metric = ['Mattes', 'CC']
BeginANTS.inputs.metric_weight = [1.0, 1.0]
BeginANTS.inputs.radius_or_number_of_bins = [32, 5]
BeginANTS.inputs.number_of_iterations = [[1000, 1000, 1000], [50, 35, 15]]
BeginANTS.inputs.use_histogram_matching = [True, True]
BeginANTS.inputs.use_estimate_learning_rate_once = [False, False]
BeginANTS.inputs.shrink_factors = [[3, 2, 1], [3, 2, 1]]
BeginANTS.inputs.smoothing_sigmas = [[3, 2, 0], [3, 2, 0]]
GetMovingImagesNode = pe.Node(interface=util.Function(
function=GetMovingImages,
input_names=[
'ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping'
],
output_names=['moving_images', 'moving_interpolation_type']),
run_without_submitting=True,
name='99_GetMovingImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetMovingImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetMovingImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
GetMovingImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', BeginANTS,
'moving_image')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_interpolation_type',
BeginANTS, 'interpolation')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image', BeginANTS,
'fixed_image')
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=['transforms', 'invert_transform_flags', 'input_image'],
name='wimtdeformed')
wimtdeformed.inputs.interpolation = 'Linear'
wimtdeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms',
wimtdeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags',
wimtdeformed,
'invert_transform_flags')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', wimtdeformed,
'input_image')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image',
wimtdeformed, 'reference_image')
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(),
name='AvgDeformedImages')
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = str(
iterationPhasePrefix) + '.nii.gz'
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(wimtdeformed, "output_image",
AvgDeformedImages, 'images')
## Now average all affine transforms together
AvgAffineTransform = pe.Node(interface=AverageAffineTransform(),
name='AvgAffineTransform')
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = 'Avererage_' + str(
iterationPhasePrefix) + '_Affine.mat'
SplitAffineAndWarpsNode = pe.Node(interface=util.Function(
function=SplitAffineAndWarpComponents,
input_names=['list_of_transforms_lists'],
output_names=['affine_component_list', 'warp_component_list']),
run_without_submitting=True,
name='99_SplitAffineAndWarpsNode')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms',
SplitAffineAndWarpsNode,
'list_of_transforms_lists')
TemplateBuildSingleIterationWF.connect(SplitAffineAndWarpsNode,
'affine_component_list',
AvgAffineTransform, 'transforms')
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name='AvgWarpImages')
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = str(
iterationPhasePrefix) + 'warp.nii.gz'
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(SplitAffineAndWarpsNode,
'warp_component_list',
AvgWarpImages, 'images')
## Now average the images together
## TODO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(interface=MultiplyImages(),
name='GradientStepWarpImage')
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = 'GradientStep0.25_' + str(
iterationPhasePrefix) + '_warp.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgWarpImages,
'output_average_image',
GradientStepWarpImage,
'first_input')
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(interface=ApplyTransforms(),
name='UpdateTemplateShape')
UpdateTemplateShape.inputs.invert_transform_flags = [True]
UpdateTemplateShape.inputs.interpolation = 'Linear'
UpdateTemplateShape.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
UpdateTemplateShape,
'reference_image')
TemplateBuildSingleIterationWF.connect([
(AvgAffineTransform, UpdateTemplateShape,
[(('affine_transform', makeListOfOneElement), 'transforms')]),
])
TemplateBuildSingleIterationWF.connect(GradientStepWarpImage,
'output_product_image',
UpdateTemplateShape, 'input_image')
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=MakeTransformListWithGradientWarps,
input_names=['averageAffineTranform', 'gradientStepWarp'],
output_names=['TransformListWithGradientWarps']),
run_without_submitting=True,
name='99_MakeTransformListWithGradientWarps')
ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform, 'affine_transform',
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'averageAffineTranform')
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape, 'output_image',
ApplyInvAverageAndFourTimesGradientStepWarpImage, 'gradientStepWarp')
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=ApplyTransforms(), name='ReshapeAverageImageWithShapeUpdate')
ReshapeAverageImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAverageImageWithShapeUpdate.inputs.interpolation = 'Linear'
ReshapeAverageImageWithShapeUpdate.default_value = 0
ReshapeAverageImageWithShapeUpdate.inputs.output_image = 'ReshapeAverageImageWithShapeUpdate.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'input_image')
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps', ReshapeAverageImageWithShapeUpdate,
'transforms')
TemplateBuildSingleIterationWF.connect(ReshapeAverageImageWithShapeUpdate,
'output_image', outputSpec,
'template')
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(function=FlattenTransformAndImagesList,
input_names=[
'ListOfPassiveImagesDictionaries', 'transforms',
'invert_transform_flags', 'interpolationMapping'
],
output_names=[
'flattened_images', 'flattened_transforms',
'flattened_invert_transform_flags',
'flattened_image_nametypes',
'flattened_interpolation_type'
]),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList")
GetPassiveImagesNode = pe.Node(interface=util.Function(
function=GetPassiveImages,
input_names=['ListOfImagesDictionaries', 'registrationImageTypes'],
output_names=['ListOfPassiveImagesDictionaries']),
run_without_submitting=True,
name='99_GetPassiveImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetPassiveImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetPassiveImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(GetPassiveImagesNode,
'ListOfPassiveImagesDictionaries',
FlattenTransformAndImagesListNode,
'ListOfPassiveImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
FlattenTransformAndImagesListNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms',
FlattenTransformAndImagesListNode,
'transforms')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags',
FlattenTransformAndImagesListNode,
'invert_transform_flags')
wimtPassivedeformed = pe.MapNode(interface=ApplyTransforms(),
iterfield=[
'transforms',
'invert_transform_flags',
'input_image', 'interpolation'
],
name='wimtPassivedeformed')
wimtPassivedeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
wimtPassivedeformed,
'reference_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_interpolation_type',
wimtPassivedeformed,
'interpolation')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_images',
wimtPassivedeformed, 'input_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_transforms',
wimtPassivedeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_invert_transform_flags',
wimtPassivedeformed,
'invert_transform_flags')
RenestDeformedPassiveImagesNode = pe.Node(
Function(function=RenestDeformedPassiveImages,
input_names=[
'deformedPassiveImages', 'flattened_image_nametypes',
'interpolationMapping'
],
output_names=[
'nested_imagetype_list', 'outputAverageImageName_list',
'image_type_list', 'nested_interpolation_type'
]),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages")
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
RenestDeformedPassiveImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(wimtPassivedeformed, 'output_image',
RenestDeformedPassiveImagesNode,
'deformedPassiveImages')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_image_nametypes',
RenestDeformedPassiveImagesNode,
'flattened_image_nametypes')
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=['images', 'output_average_image'],
name='AvgDeformedPassiveImages')
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages, 'images')
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
'output_average_image')
## -- TODO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=ApplyTransforms(),
iterfield=[
'input_image', 'reference_image', 'output_image', 'interpolation'
],
name='ReshapeAveragePassiveImageWithShapeUpdate')
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAveragePassiveImageWithShapeUpdate.default_value = 0
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'nested_interpolation_type',
ReshapeAveragePassiveImageWithShapeUpdate, 'interpolation')
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'outputAverageImageName_list',
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'input_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps',
ReshapeAveragePassiveImageWithShapeUpdate, 'transforms')
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image', outputSpec,
'passive_deformed_templates')
return TemplateBuildSingleIterationWF
def BAWantsRegistrationTemplateBuildSingleIterationWF(iterationPhasePrefix,
CLUSTER_QUEUE,
CLUSTER_QUEUE_LONG):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
inputspec.interpolationMapping :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name='antsRegistrationTemplateBuildSingleIterationWF_' +
str(iterationPhasePrefix))
inputSpec = pe.Node(
interface=util.IdentityInterface(fields=[
'ListOfImagesDictionaries',
'registrationImageTypes',
# 'maskRegistrationImageType',
'interpolationMapping',
'fixed_image'
]),
run_without_submitting=True,
name='inputspec')
## HACK: TODO: We need to have the AVG_AIR.nii.gz be warped with a default voxel value of 1.0
## HACK: TODO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: TODO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: TODO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(interface=util.IdentityInterface(
fields=['template', 'transforms_list', 'passive_deformed_templates']),
run_without_submitting=True,
name='outputspec')
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(interface=Registration(),
name='BeginANTS',
iterfield=['moving_image'])
# SEE template.py many_cpu_BeginANTS_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,4,2,8), 'overwrite': True}
## This is set in the template.py file BeginANTS.plugin_args = BeginANTS_cpu_sge_options_dictionary
CommonANTsRegistrationSettings(
antsRegistrationNode=BeginANTS,
registrationTypeDescription="SixStageAntsRegistrationT1Only",
output_transform_prefix=str(iterationPhasePrefix) + '_tfm',
output_warped_image='atlas2subject.nii.gz',
output_inverse_warped_image='subject2atlas.nii.gz',
save_state='SavedantsRegistrationNodeSyNState.h5',
invert_initial_moving_transform=False,
initial_moving_transform=None)
GetMovingImagesNode = pe.Node(interface=util.Function(
function=GetMovingImages,
input_names=[
'ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping'
],
output_names=['moving_images', 'moving_interpolation_type']),
run_without_submitting=True,
name='99_GetMovingImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetMovingImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetMovingImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
GetMovingImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', BeginANTS,
'moving_image')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_interpolation_type',
BeginANTS, 'interpolation')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image', BeginANTS,
'fixed_image')
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=['transforms', 'input_image'],
# iterfield=['transforms', 'invert_transform_flags', 'input_image'],
name='wimtdeformed')
wimtdeformed.inputs.interpolation = 'Linear'
wimtdeformed.default_value = 0
# HACK: Should try using forward_composite_transform
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transform', wimtdeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
wimtdeformed, 'transforms')
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', wimtdeformed, 'invert_transform_flags')
## NOTE: forward_invert_flags:: List of flags corresponding to the forward transforms
# wimtdeformed.inputs.invert_transform_flags = [False,False,False,False,False]
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', wimtdeformed,
'input_image')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image',
wimtdeformed, 'reference_image')
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(),
name='AvgDeformedImages')
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = str(
iterationPhasePrefix) + '.nii.gz'
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(wimtdeformed, "output_image",
AvgDeformedImages, 'images')
## Now average all affine transforms together
AvgAffineTransform = pe.Node(interface=AverageAffineTransform(),
name='AvgAffineTransform')
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = 'Avererage_' + str(
iterationPhasePrefix) + '_Affine.h5'
SplitCompositeTransform = pe.MapNode(interface=util.Function(
function=SplitCompositeToComponentTransforms,
input_names=['transformFilename'],
output_names=['affine_component_list', 'warp_component_list']),
iterfield=['transformFilename'],
run_without_submitting=True,
name='99_SplitCompositeTransform')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
SplitCompositeTransform,
'transformFilename')
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms', SplitCompositeTransform, 'transformFilename')
TemplateBuildSingleIterationWF.connect(SplitCompositeTransform,
'affine_component_list',
AvgAffineTransform, 'transforms')
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name='AvgWarpImages')
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = str(
iterationPhasePrefix) + 'warp.nii.gz'
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(SplitCompositeTransform,
'warp_component_list',
AvgWarpImages, 'images')
## Now average the images together
## TODO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(interface=MultiplyImages(),
name='GradientStepWarpImage')
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = 'GradientStep0.25_' + str(
iterationPhasePrefix) + '_warp.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgWarpImages,
'output_average_image',
GradientStepWarpImage,
'first_input')
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(interface=ApplyTransforms(),
name='UpdateTemplateShape')
UpdateTemplateShape.inputs.invert_transform_flags = [True]
UpdateTemplateShape.inputs.interpolation = 'Linear'
UpdateTemplateShape.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
UpdateTemplateShape,
'reference_image')
TemplateBuildSingleIterationWF.connect([
(AvgAffineTransform, UpdateTemplateShape,
[(('affine_transform', makeListOfOneElement), 'transforms')]),
])
TemplateBuildSingleIterationWF.connect(GradientStepWarpImage,
'output_product_image',
UpdateTemplateShape, 'input_image')
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=MakeTransformListWithGradientWarps,
input_names=['averageAffineTranform', 'gradientStepWarp'],
output_names=['TransformListWithGradientWarps']),
run_without_submitting=True,
name='99_MakeTransformListWithGradientWarps')
ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform, 'affine_transform',
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'averageAffineTranform')
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape, 'output_image',
ApplyInvAverageAndFourTimesGradientStepWarpImage, 'gradientStepWarp')
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=ApplyTransforms(), name='ReshapeAverageImageWithShapeUpdate')
ReshapeAverageImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAverageImageWithShapeUpdate.inputs.interpolation = 'Linear'
ReshapeAverageImageWithShapeUpdate.default_value = 0
ReshapeAverageImageWithShapeUpdate.inputs.output_image = 'ReshapeAverageImageWithShapeUpdate.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'input_image')
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps', ReshapeAverageImageWithShapeUpdate,
'transforms')
TemplateBuildSingleIterationWF.connect(ReshapeAverageImageWithShapeUpdate,
'output_image', outputSpec,
'template')
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(function=FlattenTransformAndImagesList,
input_names=[
'ListOfPassiveImagesDictionaries', 'transforms',
'interpolationMapping', 'invert_transform_flags'
],
output_names=[
'flattened_images', 'flattened_transforms',
'flattened_invert_transform_flags',
'flattened_image_nametypes',
'flattened_interpolation_type'
]),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList")
GetPassiveImagesNode = pe.Node(interface=util.Function(
function=GetPassiveImages,
input_names=['ListOfImagesDictionaries', 'registrationImageTypes'],
output_names=['ListOfPassiveImagesDictionaries']),
run_without_submitting=True,
name='99_GetPassiveImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetPassiveImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetPassiveImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(GetPassiveImagesNode,
'ListOfPassiveImagesDictionaries',
FlattenTransformAndImagesListNode,
'ListOfPassiveImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
FlattenTransformAndImagesListNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
FlattenTransformAndImagesListNode,
'transforms')
## FlattenTransformAndImagesListNode.inputs.invert_transform_flags = [False,False,False,False,False,False]
## TODO: Please check of invert_transform_flags has a fixed number.
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', FlattenTransformAndImagesListNode, 'invert_transform_flags')
wimtPassivedeformed = pe.MapNode(interface=ApplyTransforms(),
iterfield=[
'transforms',
'invert_transform_flags',
'input_image', 'interpolation'
],
name='wimtPassivedeformed')
wimtPassivedeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
wimtPassivedeformed,
'reference_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_interpolation_type',
wimtPassivedeformed,
'interpolation')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_images',
wimtPassivedeformed, 'input_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_transforms',
wimtPassivedeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_invert_transform_flags',
wimtPassivedeformed,
'invert_transform_flags')
RenestDeformedPassiveImagesNode = pe.Node(
Function(function=RenestDeformedPassiveImages,
input_names=[
'deformedPassiveImages', 'flattened_image_nametypes',
'interpolationMapping'
],
output_names=[
'nested_imagetype_list', 'outputAverageImageName_list',
'image_type_list', 'nested_interpolation_type'
]),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages")
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
RenestDeformedPassiveImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(wimtPassivedeformed, 'output_image',
RenestDeformedPassiveImagesNode,
'deformedPassiveImages')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_image_nametypes',
RenestDeformedPassiveImagesNode,
'flattened_image_nametypes')
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=['images', 'output_average_image'],
name='AvgDeformedPassiveImages')
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages, 'images')
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
'output_average_image')
## -- TODO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=ApplyTransforms(),
iterfield=[
'input_image', 'reference_image', 'output_image', 'interpolation'
],
name='ReshapeAveragePassiveImageWithShapeUpdate')
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAveragePassiveImageWithShapeUpdate.default_value = 0
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'nested_interpolation_type',
ReshapeAveragePassiveImageWithShapeUpdate, 'interpolation')
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'outputAverageImageName_list',
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'input_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps',
ReshapeAveragePassiveImageWithShapeUpdate, 'transforms')
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image', outputSpec,
'passive_deformed_templates')
return TemplateBuildSingleIterationWF
def ANTSTemplateBuildSingleIterationWF(iterationPhasePrefix=''):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name='ANTSTemplateBuildSingleIterationWF_' +
str(str(iterationPhasePrefix)))
inputSpec = pe.Node(interface=util.IdentityInterface(
fields=['images', 'fixed_image', 'ListOfPassiveImagesDictionaries']),
run_without_submitting=True,
name='inputspec')
## HACK: TODO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: TODO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: TODO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(interface=util.IdentityInterface(
fields=['template', 'transforms_list', 'passive_deformed_templates']),
run_without_submitting=True,
name='outputspec')
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(interface=ANTS(),
name='BeginANTS',
iterfield=['moving_image'])
BeginANTS.inputs.dimension = 3
BeginANTS.inputs.output_transform_prefix = str(
iterationPhasePrefix) + '_tfm'
BeginANTS.inputs.metric = ['CC']
BeginANTS.inputs.metric_weight = [1.0]
BeginANTS.inputs.radius = [5]
BeginANTS.inputs.transformation_model = 'SyN'
BeginANTS.inputs.gradient_step_length = 0.25
BeginANTS.inputs.number_of_iterations = [50, 35, 15]
BeginANTS.inputs.number_of_affine_iterations = [
10000, 10000, 10000, 10000, 10000
]
BeginANTS.inputs.use_histogram_matching = True
BeginANTS.inputs.mi_option = [32, 16000]
BeginANTS.inputs.regularization = 'Gauss'
BeginANTS.inputs.regularization_gradient_field_sigma = 3
BeginANTS.inputs.regularization_deformation_field_sigma = 0
TemplateBuildSingleIterationWF.connect(inputSpec, 'images', BeginANTS,
'moving_image')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image', BeginANTS,
'fixed_image')
MakeTransformsLists = pe.Node(interface=util.Function(
function=MakeListsOfTransformLists,
input_names=['warpTransformList', 'AffineTransformList'],
output_names=['out']),
run_without_submitting=True,
name='MakeTransformsLists')
MakeTransformsLists.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(BeginANTS, 'warp_transform',
MakeTransformsLists,
'warpTransformList')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'affine_transform',
MakeTransformsLists,
'AffineTransformList')
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=WarpImageMultiTransform(),
iterfield=['transformation_series', 'input_image'],
name='wimtdeformed')
TemplateBuildSingleIterationWF.connect(inputSpec, 'images', wimtdeformed,
'input_image')
TemplateBuildSingleIterationWF.connect(MakeTransformsLists, 'out',
wimtdeformed,
'transformation_series')
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(),
name='AvgDeformedImages')
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = str(
iterationPhasePrefix) + '.nii.gz'
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(wimtdeformed, "output_image",
AvgDeformedImages, 'images')
## Now average all affine transforms together
AvgAffineTransform = pe.Node(interface=AverageAffineTransform(),
name='AvgAffineTransform')
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = 'Avererage_' + str(
iterationPhasePrefix) + '_Affine.mat'
TemplateBuildSingleIterationWF.connect(BeginANTS, 'affine_transform',
AvgAffineTransform, 'transforms')
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name='AvgWarpImages')
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = str(
iterationPhasePrefix) + 'warp.nii.gz'
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(BeginANTS, 'warp_transform',
AvgWarpImages, 'images')
## Now average the images together
## TODO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(interface=MultiplyImages(),
name='GradientStepWarpImage')
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = 'GradientStep0.25_' + str(
iterationPhasePrefix) + '_warp.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgWarpImages,
'output_average_image',
GradientStepWarpImage,
'first_input')
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(interface=WarpImageMultiTransform(),
name='UpdateTemplateShape')
UpdateTemplateShape.inputs.invert_affine = [1]
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
UpdateTemplateShape,
'reference_image')
TemplateBuildSingleIterationWF.connect(AvgAffineTransform,
'affine_transform',
UpdateTemplateShape,
'transformation_series')
TemplateBuildSingleIterationWF.connect(GradientStepWarpImage,
'output_product_image',
UpdateTemplateShape, 'input_image')
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=MakeTransformListWithGradientWarps,
input_names=['averageAffineTranform', 'gradientStepWarp'],
output_names=['TransformListWithGradientWarps']),
run_without_submitting=True,
name='MakeTransformListWithGradientWarps')
ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform, 'affine_transform',
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'averageAffineTranform')
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape, 'output_image',
ApplyInvAverageAndFourTimesGradientStepWarpImage, 'gradientStepWarp')
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=WarpImageMultiTransform(),
name='ReshapeAverageImageWithShapeUpdate')
ReshapeAverageImageWithShapeUpdate.inputs.invert_affine = [1]
ReshapeAverageImageWithShapeUpdate.inputs.out_postfix = '_Reshaped'
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'input_image')
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps', ReshapeAverageImageWithShapeUpdate,
'transformation_series')
TemplateBuildSingleIterationWF.connect(ReshapeAverageImageWithShapeUpdate,
'output_image', outputSpec,
'template')
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(function=FlattenTransformAndImagesList,
input_names=[
'ListOfPassiveImagesDictionaries', 'transformation_series'
],
output_names=[
'flattened_images', 'flattened_transforms',
'flattened_image_nametypes'
]),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList")
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfPassiveImagesDictionaries',
FlattenTransformAndImagesListNode,
'ListOfPassiveImagesDictionaries')
TemplateBuildSingleIterationWF.connect(MakeTransformsLists, 'out',
FlattenTransformAndImagesListNode,
'transformation_series')
wimtPassivedeformed = pe.MapNode(
interface=WarpImageMultiTransform(),
iterfield=['transformation_series', 'input_image'],
name='wimtPassivedeformed')
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
wimtPassivedeformed,
'reference_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_images',
wimtPassivedeformed, 'input_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_transforms',
wimtPassivedeformed,
'transformation_series')
RenestDeformedPassiveImagesNode = pe.Node(
Function(
function=RenestDeformedPassiveImages,
input_names=['deformedPassiveImages', 'flattened_image_nametypes'],
output_names=[
'nested_imagetype_list', 'outputAverageImageName_list',
'image_type_list'
]),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages")
TemplateBuildSingleIterationWF.connect(wimtPassivedeformed, 'output_image',
RenestDeformedPassiveImagesNode,
'deformedPassiveImages')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_image_nametypes',
RenestDeformedPassiveImagesNode,
'flattened_image_nametypes')
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=['images', 'output_average_image'],
name='AvgDeformedPassiveImages')
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages, 'images')
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
'output_average_image')
## -- TODO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=WarpImageMultiTransform(),
iterfield=['input_image', 'reference_image', 'out_postfix'],
name='ReshapeAveragePassiveImageWithShapeUpdate')
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_affine = [1]
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, "image_type_list",
ReshapeAveragePassiveImageWithShapeUpdate, 'out_postfix')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'input_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps',
ReshapeAveragePassiveImageWithShapeUpdate, 'transformation_series')
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image', outputSpec,
'passive_deformed_templates')
return TemplateBuildSingleIterationWF
def BAWantsRegistrationTemplateBuildSingleIterationWF(iterationPhasePrefix=''):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
inputspec.interpolationMapping :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name='antsRegistrationTemplateBuildSingleIterationWF_' +
str(iterationPhasePrefix))
inputSpec = pe.Node(
interface=util.IdentityInterface(fields=[
'ListOfImagesDictionaries',
'registrationImageTypes',
#'maskRegistrationImageType',
'interpolationMapping',
'fixed_image'
]),
run_without_submitting=True,
name='inputspec')
## HACK: TODO: We need to have the AVG_AIR.nii.gz be warped with a default voxel value of 1.0
## HACK: TODO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: TODO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: TODO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(interface=util.IdentityInterface(
fields=['template', 'transforms_list', 'passive_deformed_templates']),
run_without_submitting=True,
name='outputspec')
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(interface=Registration(),
name='BeginANTS',
iterfield=['moving_image'])
BeginANTS.inputs.dimension = 3
""" This is the recommended set of parameters from the ANTS developers """
BeginANTS.inputs.output_transform_prefix = str(
iterationPhasePrefix) + '_tfm'
BeginANTS.inputs.transforms = ["Rigid", "Affine", "SyN", "SyN", "SyN"]
BeginANTS.inputs.transform_parameters = [[0.1], [0.1], [0.1, 3.0, 0.0],
[0.1, 3.0, 0.0], [0.1, 3.0, 0.0]]
BeginANTS.inputs.metric = ['MI', 'MI', 'CC', 'CC', 'CC']
BeginANTS.inputs.sampling_strategy = [
'Regular', 'Regular', None, None, None
]
BeginANTS.inputs.sampling_percentage = [0.27, 0.27, 1.0, 1.0, 1.0]
BeginANTS.inputs.metric_weight = [1.0, 1.0, 1.0, 1.0, 1.0]
BeginANTS.inputs.radius_or_number_of_bins = [32, 32, 4, 4, 4]
BeginANTS.inputs.number_of_iterations = [[1000, 1000, 1000, 1000],
[1000, 1000, 1000, 1000],
[1000, 250], [140], [25]]
BeginANTS.inputs.convergence_threshold = [5e-8, 5e-8, 5e-7, 5e-6, 5e-5]
BeginANTS.inputs.convergence_window_size = [10, 10, 10, 10, 10]
BeginANTS.inputs.use_histogram_matching = [True, True, True, True, True]
BeginANTS.inputs.shrink_factors = [[8, 4, 2, 1], [8, 4, 2, 1], [8, 4], [2],
[1]]
BeginANTS.inputs.smoothing_sigmas = [[3, 2, 1, 0], [3, 2, 1, 0], [3, 2],
[1], [0]]
BeginANTS.inputs.sigma_units = ["vox", "vox", "vox", "vox", "vox"]
BeginANTS.inputs.use_estimate_learning_rate_once = [
False, False, False, False, False
]
BeginANTS.inputs.write_composite_transform = True
BeginANTS.inputs.collapse_output_transforms = False
BeginANTS.inputs.initialize_transforms_per_stage = True
BeginANTS.inputs.winsorize_lower_quantile = 0.01
BeginANTS.inputs.winsorize_upper_quantile = 0.99
BeginANTS.inputs.output_warped_image = 'atlas2subject.nii.gz'
BeginANTS.inputs.output_inverse_warped_image = 'subject2atlas.nii.gz'
BeginANTS.inputs.save_state = 'SavedBeginANTSSyNState.h5'
BeginANTS.inputs.float = True
GetMovingImagesNode = pe.Node(interface=util.Function(
function=GetMovingImages,
input_names=[
'ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping'
],
output_names=['moving_images', 'moving_interpolation_type']),
run_without_submitting=True,
name='99_GetMovingImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetMovingImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetMovingImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
GetMovingImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', BeginANTS,
'moving_image')
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_interpolation_type',
BeginANTS, 'interpolation')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image', BeginANTS,
'fixed_image')
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=['transforms', 'input_image'],
#iterfield=['transforms', 'invert_transform_flags', 'input_image'],
name='wimtdeformed')
wimtdeformed.inputs.interpolation = 'Linear'
wimtdeformed.default_value = 0
# HACK: Should try using forward_composite_transform
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transform', wimtdeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
wimtdeformed, 'transforms')
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', wimtdeformed, 'invert_transform_flags')
## NOTE: forward_invert_flags:: List of flags corresponding to the forward transforms
#wimtdeformed.inputs.invert_transform_flags = [False,False,False,False,False]
TemplateBuildSingleIterationWF.connect(GetMovingImagesNode,
'moving_images', wimtdeformed,
'input_image')
TemplateBuildSingleIterationWF.connect(inputSpec, 'fixed_image',
wimtdeformed, 'reference_image')
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(),
name='AvgDeformedImages')
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = str(
iterationPhasePrefix) + '.nii.gz'
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(wimtdeformed, "output_image",
AvgDeformedImages, 'images')
## Now average all affine transforms together
AvgAffineTransform = pe.Node(interface=AverageAffineTransform(),
name='AvgAffineTransform')
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = 'Avererage_' + str(
iterationPhasePrefix) + '_Affine.h5'
SplitCompositeTransform = pe.MapNode(
interface=util.Function(
function=SplitCompositeToComponentTransforms,
input_names=['composite_transform_as_list'],
output_names=['affine_component_list', 'warp_component_list']),
iterfield=['composite_transform_as_list'],
run_without_submitting=True,
name='99_SplitCompositeTransform')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
SplitCompositeTransform,
'composite_transform_as_list')
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms', SplitCompositeTransform, 'composite_transform_as_list')
TemplateBuildSingleIterationWF.connect(SplitCompositeTransform,
'affine_component_list',
AvgAffineTransform, 'transforms')
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name='AvgWarpImages')
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = str(
iterationPhasePrefix) + 'warp.nii.gz'
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(SplitCompositeTransform,
'warp_component_list',
AvgWarpImages, 'images')
## Now average the images together
## TODO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(interface=MultiplyImages(),
name='GradientStepWarpImage')
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = 'GradientStep0.25_' + str(
iterationPhasePrefix) + '_warp.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgWarpImages,
'output_average_image',
GradientStepWarpImage,
'first_input')
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(interface=ApplyTransforms(),
name='UpdateTemplateShape')
UpdateTemplateShape.inputs.invert_transform_flags = [True]
UpdateTemplateShape.inputs.interpolation = 'Linear'
UpdateTemplateShape.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
UpdateTemplateShape,
'reference_image')
TemplateBuildSingleIterationWF.connect([
(AvgAffineTransform, UpdateTemplateShape,
[(('affine_transform', makeListOfOneElement), 'transforms')]),
])
TemplateBuildSingleIterationWF.connect(GradientStepWarpImage,
'output_product_image',
UpdateTemplateShape, 'input_image')
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=MakeTransformListWithGradientWarps,
input_names=['averageAffineTranform', 'gradientStepWarp'],
output_names=['TransformListWithGradientWarps']),
run_without_submitting=True,
name='99_MakeTransformListWithGradientWarps')
ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform, 'affine_transform',
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'averageAffineTranform')
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape, 'output_image',
ApplyInvAverageAndFourTimesGradientStepWarpImage, 'gradientStepWarp')
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=ApplyTransforms(), name='ReshapeAverageImageWithShapeUpdate')
ReshapeAverageImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAverageImageWithShapeUpdate.inputs.interpolation = 'Linear'
ReshapeAverageImageWithShapeUpdate.default_value = 0
ReshapeAverageImageWithShapeUpdate.inputs.output_image = 'ReshapeAverageImageWithShapeUpdate.nii.gz'
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'input_image')
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
ReshapeAverageImageWithShapeUpdate,
'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps', ReshapeAverageImageWithShapeUpdate,
'transforms')
TemplateBuildSingleIterationWF.connect(ReshapeAverageImageWithShapeUpdate,
'output_image', outputSpec,
'template')
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(function=FlattenTransformAndImagesList,
input_names=[
'ListOfPassiveImagesDictionaries', 'transforms',
'interpolationMapping', 'invert_transform_flags'
],
output_names=[
'flattened_images', 'flattened_transforms',
'flattened_invert_transform_flags',
'flattened_image_nametypes',
'flattened_interpolation_type'
]),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList")
GetPassiveImagesNode = pe.Node(interface=util.Function(
function=GetPassiveImages,
input_names=['ListOfImagesDictionaries', 'registrationImageTypes'],
output_names=['ListOfPassiveImagesDictionaries']),
run_without_submitting=True,
name='99_GetPassiveImagesNode')
TemplateBuildSingleIterationWF.connect(inputSpec,
'ListOfImagesDictionaries',
GetPassiveImagesNode,
'ListOfImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'registrationImageTypes',
GetPassiveImagesNode,
'registrationImageTypes')
TemplateBuildSingleIterationWF.connect(GetPassiveImagesNode,
'ListOfPassiveImagesDictionaries',
FlattenTransformAndImagesListNode,
'ListOfPassiveImagesDictionaries')
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
FlattenTransformAndImagesListNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(BeginANTS, 'composite_transform',
FlattenTransformAndImagesListNode,
'transforms')
## FlattenTransformAndImagesListNode.inputs.invert_transform_flags = [False,False,False,False,False,False]
## TODO: Please check of invert_transform_flags has a fixed number.
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', FlattenTransformAndImagesListNode, 'invert_transform_flags')
wimtPassivedeformed = pe.MapNode(interface=ApplyTransforms(),
iterfield=[
'transforms',
'invert_transform_flags',
'input_image', 'interpolation'
],
name='wimtPassivedeformed')
wimtPassivedeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(AvgDeformedImages,
'output_average_image',
wimtPassivedeformed,
'reference_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_interpolation_type',
wimtPassivedeformed,
'interpolation')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_images',
wimtPassivedeformed, 'input_image')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_transforms',
wimtPassivedeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_invert_transform_flags',
wimtPassivedeformed,
'invert_transform_flags')
RenestDeformedPassiveImagesNode = pe.Node(
Function(function=RenestDeformedPassiveImages,
input_names=[
'deformedPassiveImages', 'flattened_image_nametypes',
'interpolationMapping'
],
output_names=[
'nested_imagetype_list', 'outputAverageImageName_list',
'image_type_list', 'nested_interpolation_type'
]),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages")
TemplateBuildSingleIterationWF.connect(inputSpec, 'interpolationMapping',
RenestDeformedPassiveImagesNode,
'interpolationMapping')
TemplateBuildSingleIterationWF.connect(wimtPassivedeformed, 'output_image',
RenestDeformedPassiveImagesNode,
'deformedPassiveImages')
TemplateBuildSingleIterationWF.connect(FlattenTransformAndImagesListNode,
'flattened_image_nametypes',
RenestDeformedPassiveImagesNode,
'flattened_image_nametypes')
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=['images', 'output_average_image'],
name='AvgDeformedPassiveImages')
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages, 'images')
TemplateBuildSingleIterationWF.connect(RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
'output_average_image')
## -- TODO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=ApplyTransforms(),
iterfield=[
'input_image', 'reference_image', 'output_image', 'interpolation'
],
name='ReshapeAveragePassiveImageWithShapeUpdate')
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_transform_flags = [
True, False, False, False, False
]
ReshapeAveragePassiveImageWithShapeUpdate.default_value = 0
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'nested_interpolation_type',
ReshapeAveragePassiveImageWithShapeUpdate, 'interpolation')
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode, 'outputAverageImageName_list',
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'input_image')
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages, 'output_average_image',
ReshapeAveragePassiveImageWithShapeUpdate, 'reference_image')
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
'TransformListWithGradientWarps',
ReshapeAveragePassiveImageWithShapeUpdate, 'transforms')
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate, 'output_image', outputSpec,
'passive_deformed_templates')
return TemplateBuildSingleIterationWF
def baw_ants_registration_template_build_single_iteration_wf(
iterationPhasePrefix, CLUSTER_QUEUE, CLUSTER_QUEUE_LONG
):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
inputspec.interpolationMapping :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name="antsRegistrationTemplateBuildSingleIterationWF_"
+ str(iterationPhasePrefix)
)
inputSpec = pe.Node(
interface=util.IdentityInterface(
fields=[
"ListOfImagesDictionaries",
"registrationImageTypes",
# 'maskRegistrationImageType',
"interpolationMapping",
"fixed_image",
]
),
run_without_submitting=True,
name="inputspec",
)
## HACK: INFO: We need to have the AVG_AIR.nii.gz be warped with a default voxel value of 1.0
## HACK: INFO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: INFO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: INFO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(
interface=util.IdentityInterface(
fields=["template", "transforms_list", "passive_deformed_templates"]
),
run_without_submitting=True,
name="outputspec",
)
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(
interface=Registration(), name="BeginANTS", iterfield=["moving_image"]
)
# SEE template.py many_cpu_BeginANTS_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,4,2,8), 'overwrite': True}
## This is set in the template.py file BeginANTS.plugin_args = BeginANTS_cpu_sge_options_dictionary
common_ants_registration_settings(
antsRegistrationNode=BeginANTS,
registrationTypeDescription="SixStageAntsRegistrationT1Only",
output_transform_prefix=str(iterationPhasePrefix) + "_tfm",
output_warped_image="atlas2subject.nii.gz",
output_inverse_warped_image="subject2atlas.nii.gz",
save_state="SavedantsRegistrationNodeSyNState.h5",
invert_initial_moving_transform=False,
initial_moving_transform=None,
)
GetMovingImagesNode = pe.Node(
interface=util.Function(
function=get_moving_images,
input_names=[
"ListOfImagesDictionaries",
"registrationImageTypes",
"interpolationMapping",
],
output_names=["moving_images", "moving_interpolation_type"],
),
run_without_submitting=True,
name="99_GetMovingImagesNode",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"ListOfImagesDictionaries",
GetMovingImagesNode,
"ListOfImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"registrationImageTypes",
GetMovingImagesNode,
"registrationImageTypes",
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "interpolationMapping", GetMovingImagesNode, "interpolationMapping"
)
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_images", BeginANTS, "moving_image"
)
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_interpolation_type", BeginANTS, "interpolation"
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "fixed_image", BeginANTS, "fixed_image"
)
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=["transforms", "input_image"],
# iterfield=['transforms', 'invert_transform_flags', 'input_image'],
name="wimtdeformed",
)
wimtdeformed.inputs.interpolation = "Linear"
wimtdeformed.default_value = 0
# HACK: Should try using forward_composite_transform
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transform', wimtdeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(
BeginANTS, "composite_transform", wimtdeformed, "transforms"
)
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', wimtdeformed, 'invert_transform_flags')
## NOTE: forward_invert_flags:: List of flags corresponding to the forward transforms
# wimtdeformed.inputs.invert_transform_flags = [False,False,False,False,False]
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_images", wimtdeformed, "input_image"
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "fixed_image", wimtdeformed, "reference_image"
)
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(), name="AvgDeformedImages")
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = (
str(iterationPhasePrefix) + ".nii.gz"
)
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(
wimtdeformed, "output_image", AvgDeformedImages, "images"
)
## Now average all affine transforms together
AvgAffineTransform = pe.Node(
interface=AverageAffineTransform(), name="AvgAffineTransform"
)
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = (
"Avererage_" + str(iterationPhasePrefix) + "_Affine.h5"
)
SplitCompositeTransform = pe.MapNode(
interface=util.Function(
function=split_composite_to_component_transform,
input_names=["transformFilename"],
output_names=["affine_component_list", "warp_component_list"],
),
iterfield=["transformFilename"],
run_without_submitting=True,
name="99_SplitCompositeTransform",
)
TemplateBuildSingleIterationWF.connect(
BeginANTS, "composite_transform", SplitCompositeTransform, "transformFilename"
)
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms', SplitCompositeTransform, 'transformFilename')
TemplateBuildSingleIterationWF.connect(
SplitCompositeTransform,
"affine_component_list",
AvgAffineTransform,
"transforms",
)
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name="AvgWarpImages")
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = (
str(iterationPhasePrefix) + "warp.nii.gz"
)
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(
SplitCompositeTransform, "warp_component_list", AvgWarpImages, "images"
)
## Now average the images together
## INFO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(
interface=MultiplyImages(), name="GradientStepWarpImage"
)
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = (
"GradientStep0.25_" + str(iterationPhasePrefix) + "_warp.nii.gz"
)
TemplateBuildSingleIterationWF.connect(
AvgWarpImages, "output_average_image", GradientStepWarpImage, "first_input"
)
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(
interface=ApplyTransforms(), name="UpdateTemplateShape"
)
UpdateTemplateShape.inputs.invert_transform_flags = [True]
UpdateTemplateShape.inputs.interpolation = "Linear"
UpdateTemplateShape.default_value = 0
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
UpdateTemplateShape,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
[
(
AvgAffineTransform,
UpdateTemplateShape,
[(("affine_transform", make_list_of_one_element), "transforms")],
)
]
)
TemplateBuildSingleIterationWF.connect(
GradientStepWarpImage,
"output_product_image",
UpdateTemplateShape,
"input_image",
)
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=make_transform_list_with_gradient_warps,
input_names=["averageAffineTranform", "gradientStepWarp"],
output_names=["TransformListWithGradientWarps"],
),
run_without_submitting=True,
name="99_MakeTransformListWithGradientWarps",
)
# ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform,
"affine_transform",
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"averageAffineTranform",
)
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape,
"output_image",
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"gradientStepWarp",
)
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=ApplyTransforms(), name="ReshapeAverageImageWithShapeUpdate"
)
ReshapeAverageImageWithShapeUpdate.inputs.invert_transform_flags = [
True,
False,
False,
False,
False,
]
ReshapeAverageImageWithShapeUpdate.inputs.interpolation = "Linear"
ReshapeAverageImageWithShapeUpdate.default_value = 0
ReshapeAverageImageWithShapeUpdate.inputs.output_image = (
"ReshapeAverageImageWithShapeUpdate.nii.gz"
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
ReshapeAverageImageWithShapeUpdate,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
ReshapeAverageImageWithShapeUpdate,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"TransformListWithGradientWarps",
ReshapeAverageImageWithShapeUpdate,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
ReshapeAverageImageWithShapeUpdate, "output_image", outputSpec, "template"
)
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(
function=flatten_transform_and_images_list,
input_names=[
"ListOfPassiveImagesDictionaries",
"transforms",
"interpolationMapping",
"invert_transform_flags",
],
output_names=[
"flattened_images",
"flattened_transforms",
"flattened_invert_transform_flags",
"flattened_image_nametypes",
"flattened_interpolation_type",
],
),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList",
)
GetPassiveImagesNode = pe.Node(
interface=util.Function(
function=get_passive_images,
input_names=["ListOfImagesDictionaries", "registrationImageTypes"],
output_names=["ListOfPassiveImagesDictionaries"],
),
run_without_submitting=True,
name="99_GetPassiveImagesNode",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"ListOfImagesDictionaries",
GetPassiveImagesNode,
"ListOfImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"registrationImageTypes",
GetPassiveImagesNode,
"registrationImageTypes",
)
TemplateBuildSingleIterationWF.connect(
GetPassiveImagesNode,
"ListOfPassiveImagesDictionaries",
FlattenTransformAndImagesListNode,
"ListOfPassiveImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"interpolationMapping",
FlattenTransformAndImagesListNode,
"interpolationMapping",
)
TemplateBuildSingleIterationWF.connect(
BeginANTS,
"composite_transform",
FlattenTransformAndImagesListNode,
"transforms",
)
## FlattenTransformAndImagesListNode.inputs.invert_transform_flags = [False,False,False,False,False,False]
## INFO: Please check of invert_transform_flags has a fixed number.
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', FlattenTransformAndImagesListNode, 'invert_transform_flags')
wimtPassivedeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=[
"transforms",
"invert_transform_flags",
"input_image",
"interpolation",
],
name="wimtPassivedeformed",
)
wimtPassivedeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
wimtPassivedeformed,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_interpolation_type",
wimtPassivedeformed,
"interpolation",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_images",
wimtPassivedeformed,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_transforms",
wimtPassivedeformed,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_invert_transform_flags",
wimtPassivedeformed,
"invert_transform_flags",
)
RenestDeformedPassiveImagesNode = pe.Node(
Function(
function=renest_deformed_passive_images,
input_names=[
"deformedPassiveImages",
"flattened_image_nametypes",
"interpolationMapping",
],
output_names=[
"nested_imagetype_list",
"outputAverageImageName_list",
"image_type_list",
"nested_interpolation_type",
],
),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"interpolationMapping",
RenestDeformedPassiveImagesNode,
"interpolationMapping",
)
TemplateBuildSingleIterationWF.connect(
wimtPassivedeformed,
"output_image",
RenestDeformedPassiveImagesNode,
"deformedPassiveImages",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_image_nametypes",
RenestDeformedPassiveImagesNode,
"flattened_image_nametypes",
)
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=["images", "output_average_image"],
name="AvgDeformedPassiveImages",
)
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages,
"images",
)
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
"output_average_image",
)
## -- INFO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=ApplyTransforms(),
iterfield=["input_image", "reference_image", "output_image", "interpolation"],
name="ReshapeAveragePassiveImageWithShapeUpdate",
)
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_transform_flags = [
True,
False,
False,
False,
False,
]
ReshapeAveragePassiveImageWithShapeUpdate.default_value = 0
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"nested_interpolation_type",
ReshapeAveragePassiveImageWithShapeUpdate,
"interpolation",
)
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
ReshapeAveragePassiveImageWithShapeUpdate,
"output_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages,
"output_average_image",
ReshapeAveragePassiveImageWithShapeUpdate,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages,
"output_average_image",
ReshapeAveragePassiveImageWithShapeUpdate,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"TransformListWithGradientWarps",
ReshapeAveragePassiveImageWithShapeUpdate,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate,
"output_image",
outputSpec,
"passive_deformed_templates",
)
return TemplateBuildSingleIterationWF