def register_ants(moving_image, atlas, output_image):
reg = Registration()
reg.inputs.fixed_image = atlas
reg.inputs.moving_image = moving_image
reg.inputs.output_transform_prefix = 'transform'
reg.inputs.output_warped_image = output_image
reg.inputs.output_transform_prefix = "stx-152"
reg.inputs.transforms = ['Translation']
reg.inputs.transform_parameters = [(0.1, )]
reg.inputs.number_of_iterations = ([[10000, 111110, 11110]])
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.metric = ['Mattes']
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.sampling_strategy = ['Regular']
reg.inputs.sampling_percentage = [0.3]
reg.inputs.convergence_threshold = [1.e-6]
reg.inputs.convergence_window_size = [20]
reg.inputs.smoothing_sigmas = [[4, 2, 1]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[32, 16, 4]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [False]
reg.inputs.initial_moving_transform_com = True
reg.run()
def test_BaseInterface_load_save_inputs():
tmp_dir = tempfile.mkdtemp()
tmp_json = os.path.join(tmp_dir, 'settings.json')
class InputSpec(nib.TraitedSpec):
input1 = nib.traits.Int()
input2 = nib.traits.Float()
input3 = nib.traits.Bool()
input4 = nib.traits.Str()
class DerivedInterface(nib.BaseInterface):
input_spec = InputSpec
def __init__(self, **inputs):
super(DerivedInterface, self).__init__(**inputs)
inputs_dict = {'input1': 12, 'input3': True, 'input4': 'some string'}
bif = DerivedInterface(**inputs_dict)
bif.save_inputs_to_json(tmp_json)
bif2 = DerivedInterface()
bif2.load_inputs_from_json(tmp_json)
yield assert_equal, bif2.inputs.get_traitsfree(), inputs_dict
bif3 = DerivedInterface(from_file=tmp_json)
yield assert_equal, bif3.inputs.get_traitsfree(), inputs_dict
inputs_dict2 = inputs_dict.copy()
inputs_dict2.update({'input4': 'some other string'})
bif4 = DerivedInterface(from_file=tmp_json, input4=inputs_dict2['input4'])
yield assert_equal, bif4.inputs.get_traitsfree(), inputs_dict2
bif5 = DerivedInterface(input4=inputs_dict2['input4'])
bif5.load_inputs_from_json(tmp_json, overwrite=False)
yield assert_equal, bif5.inputs.get_traitsfree(), inputs_dict2
bif6 = DerivedInterface(input4=inputs_dict2['input4'])
bif6.load_inputs_from_json(tmp_json)
yield assert_equal, bif6.inputs.get_traitsfree(), inputs_dict
# test get hashval in a complex interface
from nipype.interfaces.ants import Registration
settings = example_data(
example_data('smri_ants_registration_settings.json'))
with open(settings) as setf:
data_dict = json.load(setf)
tsthash = Registration()
tsthash.load_inputs_from_json(settings)
yield assert_equal, {}, check_dict(data_dict,
tsthash.inputs.get_traitsfree())
tsthash2 = Registration(from_file=settings)
yield assert_equal, {}, check_dict(data_dict,
tsthash2.inputs.get_traitsfree())
_, hashvalue = tsthash.inputs.get_hashval(hash_method='timestamp')
yield assert_equal, 'ec5755e07287e04a4b409e03b77a517c', hashvalue
def test_BaseInterface_load_save_inputs():
tmp_dir = tempfile.mkdtemp()
tmp_json = os.path.join(tmp_dir, 'settings.json')
class InputSpec(nib.TraitedSpec):
input1 = nib.traits.Int()
input2 = nib.traits.Float()
input3 = nib.traits.Bool()
input4 = nib.traits.Str()
class DerivedInterface(nib.BaseInterface):
input_spec = InputSpec
def __init__(self, **inputs):
super(DerivedInterface, self).__init__(**inputs)
inputs_dict = {'input1': 12, 'input3': True,
'input4': 'some string'}
bif = DerivedInterface(**inputs_dict)
bif.save_inputs_to_json(tmp_json)
bif2 = DerivedInterface()
bif2.load_inputs_from_json(tmp_json)
yield assert_equal, bif2.inputs.get_traitsfree(), inputs_dict
bif3 = DerivedInterface(from_file=tmp_json)
yield assert_equal, bif3.inputs.get_traitsfree(), inputs_dict
inputs_dict2 = inputs_dict.copy()
inputs_dict2.update({'input4': 'some other string'})
bif4 = DerivedInterface(from_file=tmp_json, input4=inputs_dict2['input4'])
yield assert_equal, bif4.inputs.get_traitsfree(), inputs_dict2
bif5 = DerivedInterface(input4=inputs_dict2['input4'])
bif5.load_inputs_from_json(tmp_json, overwrite=False)
yield assert_equal, bif5.inputs.get_traitsfree(), inputs_dict2
bif6 = DerivedInterface(input4=inputs_dict2['input4'])
bif6.load_inputs_from_json(tmp_json)
yield assert_equal, bif6.inputs.get_traitsfree(), inputs_dict
# test get hashval in a complex interface
from nipype.interfaces.ants import Registration
settings = example_data(example_data('smri_ants_registration_settings.json'))
with open(settings) as setf:
data_dict = json.load(setf)
tsthash = Registration()
tsthash.load_inputs_from_json(settings)
yield assert_equal, {}, check_dict(data_dict, tsthash.inputs.get_traitsfree())
tsthash2 = Registration(from_file=settings)
yield assert_equal, {}, check_dict(data_dict, tsthash2.inputs.get_traitsfree())
_, hashvalue = tsthash.inputs.get_hashval(hash_method='timestamp')
yield assert_equal, 'ec5755e07287e04a4b409e03b77a517c', hashvalue
def test_BaseInterface_load_save_inputs(tmpdir):
tmp_json = tmpdir.join("settings.json").strpath
class InputSpec(nib.TraitedSpec):
input1 = nib.traits.Int()
input2 = nib.traits.Float()
input3 = nib.traits.Bool()
input4 = nib.traits.Str()
class DerivedInterface(nib.BaseInterface):
input_spec = InputSpec
def __init__(self, **inputs):
super(DerivedInterface, self).__init__(**inputs)
inputs_dict = {"input1": 12, "input3": True, "input4": "some string"}
bif = DerivedInterface(**inputs_dict)
bif.save_inputs_to_json(tmp_json)
bif2 = DerivedInterface()
bif2.load_inputs_from_json(tmp_json)
assert bif2.inputs.get_traitsfree() == inputs_dict
bif3 = DerivedInterface(from_file=tmp_json)
assert bif3.inputs.get_traitsfree() == inputs_dict
inputs_dict2 = inputs_dict.copy()
inputs_dict2.update({"input4": "some other string"})
bif4 = DerivedInterface(from_file=tmp_json, input4=inputs_dict2["input4"])
assert bif4.inputs.get_traitsfree() == inputs_dict2
bif5 = DerivedInterface(input4=inputs_dict2["input4"])
bif5.load_inputs_from_json(tmp_json, overwrite=False)
assert bif5.inputs.get_traitsfree() == inputs_dict2
bif6 = DerivedInterface(input4=inputs_dict2["input4"])
bif6.load_inputs_from_json(tmp_json)
assert bif6.inputs.get_traitsfree() == inputs_dict
# test get hashval in a complex interface
from nipype.interfaces.ants import Registration
settings = example_data(
example_data("smri_ants_registration_settings.json"))
with open(settings) as setf:
data_dict = json.load(setf)
tsthash = Registration()
tsthash.load_inputs_from_json(settings)
assert {} == check_dict(data_dict, tsthash.inputs.get_traitsfree())
tsthash2 = Registration(from_file=settings)
assert {} == check_dict(data_dict, tsthash2.inputs.get_traitsfree())
_, hashvalue = tsthash.inputs.get_hashval(hash_method="timestamp")
assert hashvalue == "e35bf07fea8049cc02de9235f85e8903"
def test_BaseInterface_load_save_inputs(tmpdir):
tmp_json = tmpdir.join('settings.json').strpath
class InputSpec(nib.TraitedSpec):
input1 = nib.traits.Int()
input2 = nib.traits.Float()
input3 = nib.traits.Bool()
input4 = nib.traits.Str()
class DerivedInterface(nib.BaseInterface):
input_spec = InputSpec
def __init__(self, **inputs):
super(DerivedInterface, self).__init__(**inputs)
inputs_dict = {'input1': 12, 'input3': True, 'input4': 'some string'}
bif = DerivedInterface(**inputs_dict)
bif.save_inputs_to_json(tmp_json)
bif2 = DerivedInterface()
bif2.load_inputs_from_json(tmp_json)
assert bif2.inputs.get_traitsfree() == inputs_dict
bif3 = DerivedInterface(from_file=tmp_json)
assert bif3.inputs.get_traitsfree() == inputs_dict
inputs_dict2 = inputs_dict.copy()
inputs_dict2.update({'input4': 'some other string'})
bif4 = DerivedInterface(from_file=tmp_json, input4=inputs_dict2['input4'])
assert bif4.inputs.get_traitsfree() == inputs_dict2
bif5 = DerivedInterface(input4=inputs_dict2['input4'])
bif5.load_inputs_from_json(tmp_json, overwrite=False)
assert bif5.inputs.get_traitsfree() == inputs_dict2
bif6 = DerivedInterface(input4=inputs_dict2['input4'])
bif6.load_inputs_from_json(tmp_json)
assert bif6.inputs.get_traitsfree() == inputs_dict
# test get hashval in a complex interface
from nipype.interfaces.ants import Registration
settings = example_data(
example_data('smri_ants_registration_settings.json'))
with open(settings) as setf:
data_dict = json.load(setf)
tsthash = Registration()
tsthash.load_inputs_from_json(settings)
assert {} == check_dict(data_dict, tsthash.inputs.get_traitsfree())
tsthash2 = Registration(from_file=settings)
assert {} == check_dict(data_dict, tsthash2.inputs.get_traitsfree())
_, hashvalue = tsthash.inputs.get_hashval(hash_method='timestamp')
assert '8562a5623562a871115eb14822ee8d02' == hashvalue
def test_BaseInterface_load_save_inputs(tmpdir):
tmp_json = tmpdir.join('settings.json').strpath
class InputSpec(nib.TraitedSpec):
input1 = nib.traits.Int()
input2 = nib.traits.Float()
input3 = nib.traits.Bool()
input4 = nib.traits.Str()
class DerivedInterface(nib.BaseInterface):
input_spec = InputSpec
def __init__(self, **inputs):
super(DerivedInterface, self).__init__(**inputs)
inputs_dict = {'input1': 12, 'input3': True, 'input4': 'some string'}
bif = DerivedInterface(**inputs_dict)
bif.save_inputs_to_json(tmp_json)
bif2 = DerivedInterface()
bif2.load_inputs_from_json(tmp_json)
assert bif2.inputs.get_traitsfree() == inputs_dict
bif3 = DerivedInterface(from_file=tmp_json)
assert bif3.inputs.get_traitsfree() == inputs_dict
inputs_dict2 = inputs_dict.copy()
inputs_dict2.update({'input4': 'some other string'})
bif4 = DerivedInterface(from_file=tmp_json, input4=inputs_dict2['input4'])
assert bif4.inputs.get_traitsfree() == inputs_dict2
bif5 = DerivedInterface(input4=inputs_dict2['input4'])
bif5.load_inputs_from_json(tmp_json, overwrite=False)
assert bif5.inputs.get_traitsfree() == inputs_dict2
bif6 = DerivedInterface(input4=inputs_dict2['input4'])
bif6.load_inputs_from_json(tmp_json)
assert bif6.inputs.get_traitsfree() == inputs_dict
# test get hashval in a complex interface
from nipype.interfaces.ants import Registration
settings = example_data(
example_data('smri_ants_registration_settings.json'))
with open(settings) as setf:
data_dict = json.load(setf)
tsthash = Registration()
tsthash.load_inputs_from_json(settings)
assert {} == check_dict(data_dict, tsthash.inputs.get_traitsfree())
tsthash2 = Registration(from_file=settings)
assert {} == check_dict(data_dict, tsthash2.inputs.get_traitsfree())
_, hashvalue = tsthash.inputs.get_hashval(hash_method='timestamp')
assert '8562a5623562a871115eb14822ee8d02' == hashvalue
def reg_run(fixed_image, moving_image, output_transform_prefix,
output_warped_image, ants_thread_count):
# os.environ['PATH']+=':/path_to_antsbin'
reg = Registration()
reg.inputs.fixed_image = fixed_image
reg.inputs.moving_image = moving_image
reg.inputs.output_transform_prefix = output_transform_prefix
reg.inputs.transforms = ['SyN']
reg.inputs.transform_parameters = [(0.01, )]
reg.inputs.number_of_iterations = [[200, 200, 200, 200, 150, 50]]
# reg.inputs.number_of_iterations = [[50,50,50,40,30,20]]
reg.inputs.dimension = 2
reg.inputs.num_threads = ants_thread_count
reg.inputs.metric = ['Mattes']
# Default (value ignored currently by ANTs)
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.sampling_strategy = ['Regular']
reg.inputs.sampling_percentage = [1.0] # 0.3]
reg.inputs.convergence_threshold = [1.e-8]
reg.inputs.convergence_window_size = [10]
reg.inputs.smoothing_sigmas = [[6, 5, 4, 3, 2, 1]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[6, 5, 4, 3, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [True] # This is the default
reg.inputs.output_warped_image = output_warped_image
reg1 = copy.deepcopy(reg)
reg1.cmdline
reg1.run()
def calculateRigidTransforms(frame1, frame2, saveFn):
"""
Given the pair of images, calculate the rigid transformation from frame2
to frame1 and save it using the saveFn prefix.
Inputs:
- frame1: image at timepoint n
- frame2: image at timepoint n+1
- saveFn: the prefix filename where the transform will be saved
"""
# set up the registration
reg = Registration()
reg.inputs.fixed_image = frame1
reg.inputs.moving_image = frame2
reg.inputs.output_transform_prefix = saveFn
reg.inputs.interpolation = 'NearestNeighbor'
reg.inputs.transforms = ['Rigid']
reg.inputs.transform_parameters = [(0.1, )]
reg.inputs.number_of_iterations = [[100, 20]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = False
reg.inputs.collapse_output_transforms = True
reg.inputs.initialize_transforms_per_stage = False
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [5]
reg.inputs.sampling_strategy = ['Random']
reg.inputs.sampling_percentage = [0.05]
reg.inputs.convergence_threshold = [1.e-2]
reg.inputs.convergence_window_size = [20]
reg.inputs.smoothing_sigmas = [[2, 1]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [True]
reg.inputs.output_warped_image = False
reg.inputs.num_threads = 50
# run the registration
reg.run()
def rigidRegFunc(path,fileName,input_fixed,input_moving):
os.chdir(path)
reg = Registration()
# ants-registration parameters:
reg.inputs.fixed_image = input_fixed # fixed image
reg.inputs.moving_image = input_moving # moving image
reg.inputs.output_transform_prefix = path # file path
reg.inputs.transforms = ['Rigid'] # list of transformations
reg.inputs.transform_parameters = [(.5,)]
reg.inputs.number_of_iterations = [[40, 20, 10]]
# reg.inputs.number_of_iterations = [[1, 1, 1]]
reg.inputs.dimension = 3
reg.inputs.initial_moving_transform_com = True
#reg.inputs.invert_initial_moving_transform = True
reg.inputs.output_warped_image = True
reg.inputs.output_inverse_warped_image = True
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.metric = ['MI'] # mutual information
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [64]
reg.inputs.sampling_strategy = ['Regular']
reg.inputs.sampling_percentage = [0.5]
reg.inputs.terminal_output = 'allatonce'
reg.inputs.convergence_threshold = [1.e-6]
reg.inputs.convergence_window_size = [10]
reg.inputs.smoothing_sigmas = [[3, 1, 0]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[ 2, 1, 0]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [True]
reg.terminal_output = 'none'
reg.inputs.num_threads = 4 # ?
reg.inputs.winsorize_lower_quantile = 0.025
reg.inputs.winsorize_upper_quantile = 0.95
reg.inputs.output_warped_image = True
#reg.inputs.collapse_linear_transforms_to_fixed_image_header = False
reg.inputs.output_warped_image = path + 'rigid_reg_'+ fileName
reg.cmdline
reg.run()
return
def __init__(self,
moving_image=['path'],
fixed_image=['path'],
metric=['CC', 'MeanSquares', 'Demons'],
metric_weight=[1.0],
transforms=['Affine', 'BSplineSyN'],
shrink_factors=[[2, 1], [3, 2, 1]],
smoothing_sigmas=[[1, 0], [2, 1, 0]], **options):
from nipype.interfaces.ants import Registration
reg = Registration()
reg.inputs.moving_image = moving_image
reg.inputs.fixed_image = fixed_image
reg.inputs.metric = metric
reg.inputs.metric_weight = metric_weight
reg.inputs.transforms = transforms
reg.inputs.shrink_factors = shrink_factors
reg.inputs.smoothing_sigmas = smoothing_sigmas
for ef in options:
setattr(reg.inputs, ef, options[ef])
self.res = reg.run()
def make_w_coreg_3T_ants():
"""Contains examples on how to convert from struct to func and from func to
struct"""
w = Workflow('coreg_7T')
n_in = Node(IdentityInterface(fields=[
'T1w',
'mean',
]), name='input')
n_out = Node(IdentityInterface(fields=[
'mat_func2struct',
]),
name='output')
n_coreg = Node(Registration(), name='antsReg')
n_coreg.inputs.winsorize_lower_quantile = 0.005
n_coreg.inputs.winsorize_upper_quantile = 0.995
n_coreg.inputs.dimension = 3
n_coreg.inputs.float = True
n_coreg.inputs.transforms = ['Rigid', 'Affine']
n_coreg.inputs.transform_parameters = [[
0.1,
], [
0.1,
]]
n_coreg.inputs.metric = ['MI', 'MI']
n_coreg.inputs.metric_weight = [1, 1]
n_coreg.inputs.radius_or_number_of_bins = [32, 32]
n_coreg.inputs.sampling_strategy = ['Regular', 'Regular']
n_coreg.inputs.sampling_percentage = [0.25, 0.25]
n_coreg.inputs.sigma_units = ['vox', 'vox']
n_coreg.inputs.convergence_threshold = [1e-6, 1e-6]
n_coreg.inputs.smoothing_sigmas = [[4, 2, 0], [1, 0]]
n_coreg.inputs.shrink_factors = [[5, 3, 1], [3, 1]]
n_coreg.inputs.convergence_window_size = [20, 10]
n_coreg.inputs.number_of_iterations = [[1000, 500, 200], [500, 200]]
# n_coreg.inputs.restrict_deformation = [[], [1, 1, 0]]
n_coreg.inputs.use_histogram_matching = [True, True]
n_coreg.inputs.output_warped_image = True
n_coreg.inputs.output_inverse_warped_image = True
n_coreg.inputs.output_transform_prefix = 'ants_func_to_struct'
n_coreg.inputs.interpolation = 'Linear'
w.connect(n_in, 'T1w', n_coreg, 'fixed_image')
w.connect(n_in, 'mean', n_coreg, 'moving_image')
w.connect(n_coreg, 'forward_transforms', n_out, 'mat_func2struct')
return w
def _run_interface(self, runtime):
mnc2nii_sh = pe.Node(interface=mnc2nii_shCommand(), name="mnc2nii_sh")
nii2mnc_sh = pe.Node(interface=nii2mnc_shCommand(), name="nii2mnc_sh")
reg = pe.Node(interface=Registration(), name="registration")
mnc2nii_sh.inputs.in_file = self.inputs.moving_image
mnc2nii_sh.run()
mnc2nii_sh_nodes = []
inputs = [
"fixed_image", "fixed_image_mask", "fixed_image_mask",
"fixed_image_mask", "moving_image", "moving_image_mask",
"moving_image_masks"
]
self_inputs = [
self.inputs.fixed_image, self.inputs.fixed_image_mask,
self.inputs.fixed_image_mask, self.inputs.fixed_image_mask,
self.inputs.moving_image, self.inputs.moving_image_mask,
self.inputs.moving_image_masks
]
reg_inputs = [
reg.inputs.fixed_image, reg.inputs.fixed_image_mask,
reg.inputs.fixed_image_mask, reg.inputs.fixed_image_mask,
reg.inputs.moving_image, reg.inputs.moving_image_mask,
reg.inputs.moving_image_masks
]
for s, r, i in zip(self_inputs, reg_inputs, inputs):
if isdefined(s):
mnc2nii_sh = pe.Node(interface=mnc2nii_shCommand(),
name="mnc2nii_sh_" + i,
in_file=self.inputs.fixed_image)
mnc2nii_sh.run()
r = mnc2nii_sh.out_file
if isdefined(self.inputs.dimension):
reg.inputs.dimension = self.inputs.dimension
if isdefined(self.inputs.save_state):
reg.inputs.save_state = self.inputs.save_state
if isdefined(self.inputs.restore_state):
reg.inputs.restore_state = self.inputs.restore_state
if isdefined(self.inputs.initial_moving_transform):
reg.inputs.initial_moving_tr = self.inputs.initial_moving_tr
if isdefined(self.inputs.invert_initial_moving_transform):
reg.inputs.invert_initial_moving_tr = self.inputs.invert_initial_moving_tr
if isdefined(self.inputs.initial_moving_transform_com):
reg.inputs.initial_moving_transform_com = self.inputs.initial_moving_transform_com
if isdefined(self.inputs.metric_item_trait):
reg.inputs.metric_item_trait = self.inputs.metric_item_trait
if isdefined(self.inputs.metric_stage_trait):
reg.inputs.metric_stage_trait = self.inputs.metric_stage_trait
if isdefined(self.inputs.metric):
reg.inputs.metric = self.inputs.metric
if isdefined(self.inputs.metric_weight_item_trait):
reg.inputs.metric_weight_item_trait = self.inputs.metric_weight_item_trait
if isdefined(self.inputs.metric_weight_stage_trait):
reg.inputs.metric_weight_stage_trait = self.inputs.metric_weight_stage_trait
if isdefined(self.inputs.metric_weight):
reg.inputs.metric_weight = self.inputs.metric_weight
if isdefined(self.inputs.radius_bins_item_trait):
reg.inputs.radius_bins_item_trait = self.inputs.radius_bins_item_trait
if isdefined(self.inputs.radius_bins_stage_trait):
reg.inputs.radius_bins_stage_trait = self.inputs.radius_bins_stage_trait
if isdefined(self.inputs.radius_or_number_of_bins):
reg.inputs.radius_or_number_of_bins = self.inputs.radius_or_number_of_bins
if isdefined(self.inputs.sampling_strategy_item_trait):
reg.inputs.sampling_strategy_item_trait = self.inputs.sampling_strategy_item_trait
if isdefined(self.inputs.sampling_strategy_stage_trait):
reg.inputs.sampling_strategy_stage_trait = self.inputs.sampling_strategy_stage_trait
if isdefined(self.inputs.sampling_strategy):
reg.inputs.sampling_strategy = self.inputs.sampling_strategy
if isdefined(self.inputs.sampling_percentage_item_trait):
reg.inputs.sampling_percentage_item_trait = self.inputs.sampling_percentage_item_trait
if isdefined(self.inputs.sampling_percentage_stage_trait):
reg.inputs.sampling_percentage_stage_trait = self.inputs.sampling_percentage_stage_trait
if isdefined(self.inputs.sampling_percentage):
reg.inputs.sampling_percentage = self.inputs.sampling_percentage
if isdefined(self.inputs.use_estimate_learning_rate_once):
reg.inputs.use_estimate_learning_rate_once = self.inputs.use_estimate_learning_rate_once
if isdefined(self.inputs.use_histogram_matching):
reg.inputs.use_histogram_matching = self.inputs.use_histogram_matching
if isdefined(self.inputs.interpolation):
reg.inputs.interpolation = self.inputs.interpolation
if isdefined(self.inputs.interpolation_parameters):
reg.inputs.interpolation_parameters = self.inputs.interpolation_parameters
if isdefined(self.inputs.write_composite_transform):
reg.inputs.write_composite_transform = self.inputs.write_composite_transform
if isdefined(self.inputs.collapse_output_transforms):
reg.inputs.collapse_output_transforms = self.inputs.collapse_output_transforms
if isdefined(self.inputs.initialize_transforms_per_stage):
reg.inputs.initialize_transforms_per_stage = self.inputs.initialize_transforms_per_stage
if isdefined(self.inputs.float): reg.inputs.float = self.inputs.float
if isdefined(self.inputs.transform_parameters):
reg.inputs.transform_parameters = self.inputs.transform_parameters
if isdefined(self.inputs.restrict_deformation):
reg.inputs.restrict_deformation = self.inputs.restrict_deformation
if isdefined(self.inputs.number_of_iterations):
reg.inputs.number_of_iterations = self.inputs.number_of_iterations
if isdefined(self.inputs.smoothing_sigmas):
reg.inputs.smoothing_sigmas = self.inputs.smoothing_sigmas
if isdefined(self.inputs.sigma_units):
reg.inputs.sigma_units = self.inputs.sigma_units
if isdefined(self.inputs.shrink_factors):
reg.inputs.shrink_factors = self.inputs.shrink_factors
if isdefined(self.inputs.convergence_threshold):
reg.inputs.convergence_threshold = self.inputs.convergence_threshold
if isdefined(self.inputs.convergence_window_size):
reg.inputs.convergence_window_size = self.inputs.convergence_window_size
if isdefined(self.inputs.output_transform_prefix):
reg.inputs.output_transform_prefix = self.inputs.output_transform_prefix
if isdefined(self.inputs.output_warped_image):
reg.inputs.output_warped_image = self.inputs.output_warped_image
if isdefined(self.inputs.output_inverse_warped_image):
reg.inputs.output_inverse_warped_image = self.inputs.output_inverse_warped_image
if isdefined(self.inputs.winsorize_upper_quantile):
reg.inputs.winsorize_upper_quantile = self.inputs.winsorize_upper_quantile
if isdefined(self.inputs.winsorize_lower_quantile):
reg.inputs.winsorize_lower_quantile = self.inputs.winsorize_lower_quantile
if isdefined(self.inputs.verbose):
reg.inputs.verbose = self.inputs.verbose
reg.run()
nii2mnc_sh.inputs.in_file = reg.inputs.warped_image
nii2mnc_sh.run()
self.outputs.warped_image = nii2mnc_sh.inputs.warped_image
return (runtime)
"""Test ANTs registration interface
Parameters values are taken from: https://github.com/ANTsX/ANTs/wiki/Anatomy-of-an-antsRegistration-call
Note: Runtime is quite long with current parameters
"""
from nipype.interfaces.ants import Registration
#Get template path in your system
from nipype.interfaces.fsl import Info
template_path = Info.standard_image('MNI152_T1_1mm_brain.nii.gz')
reg = Registration()
reg.inputs.fixed_image = template_path
reg.inputs.moving_image = 'test-data/haxby2001/subj2/anat.nii.gz'
# Choose Mutual Information as the metric and relevant metric inputs
reg.inputs.metric = ['MI', 'MI', 'CC']
reg.inputs.metric_weight = [1] * 3
reg.inputs.radius_or_number_of_bins = [32] * 3
reg.inputs.sampling_strategy = ['Regular', 'Regular', None]
reg.inputs.sampling_percentage = [0.25, 0.25, None]
# Choose the type of transforms and in what order to implement
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
# Parameters are (GradientStep, updateFieldVarianceInVoxelSpace, totalFieldVarianceInVoxelSpace)
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.1, 3.0, 0.0)]
# Specify where to save results
reg.output_warped_image = 'out-ants-reg/anat_norm.nii.gz'
def make_w_coreg_7T_7T():
w = Workflow('coreg_7T_7T')
n_in = Node(IdentityInterface(fields=[
'T1w_SE',
'T1w_GE',
]), name='input')
n_out = Node(IdentityInterface(fields=[
'mat_ants',
'func',
]),
name='output')
n_coreg = Node(Registration(), name='antsReg')
n_coreg.inputs.dimension = 3
n_coreg.inputs.winsorize_lower_quantile = 0.005
n_coreg.inputs.winsorize_upper_quantile = 0.995
n_coreg.inputs.float = True
n_coreg.inputs.interpolation = 'Linear'
n_coreg.inputs.transforms = ['Rigid', 'Affine']
n_coreg.inputs.transform_parameters = [[
0.2,
], [
0.1,
]]
n_coreg.inputs.metric = ['MI', 'MI']
n_coreg.inputs.metric_weight = [1, 1]
n_coreg.inputs.radius_or_number_of_bins = [32, 32]
n_coreg.inputs.sampling_strategy = ['Regular', 'Regular']
n_coreg.inputs.sampling_percentage = [0.25, 0.25]
n_coreg.inputs.sigma_units = ['vox', 'vox']
n_coreg.inputs.convergence_threshold = [1e-6, 1e-6]
n_coreg.inputs.smoothing_sigmas = [[4, 2, 0], [1, 0]]
n_coreg.inputs.shrink_factors = [[3, 2, 1], [2, 1]]
n_coreg.inputs.convergence_window_size = [20, 10]
n_coreg.inputs.number_of_iterations = [[1000, 500, 200], [500, 200]]
n_coreg.inputs.restrict_deformation = [[], [1, 1, 0]]
n_coreg.inputs.output_warped_image = True
n_coreg.inputs.output_inverse_warped_image = True
n_ge2se = Node(ApplyTransforms(), name='ants_ge2se')
n_ge2se.inputs.dimension = 3
n_ge2se.inputs.invert_transform_flags = True
n_ge2se.inputs.interpolation = 'Linear'
n_se2ge = Node(ApplyTransforms(), name='ants_se2ge')
n_se2ge.inputs.dimension = 3
n_se2ge.inputs.interpolation = 'Linear'
n_se2ge.inputs.default_value = 0
w.connect(n_in, 'T1w_SE', n_coreg, 'moving_image')
w.connect(n_in, 'T1w_GE', n_coreg, 'fixed_image')
w.connect(n_coreg, 'forward_transforms', n_out, 'mat_ants')
w.connect(n_coreg, 'forward_transforms', n_ge2se, 'transforms')
w.connect(n_in, 'T1w_GE', n_ge2se, 'input_image')
w.connect(n_in, 'T1w_SE', n_ge2se, 'reference_image')
w.connect(n_coreg, 'forward_transforms', n_se2ge, 'transforms')
w.connect(n_in, 'T1w_SE', n_se2ge, 'input_image')
w.connect(n_in, 'T1w_GE', n_se2ge, 'reference_image')
return w
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 anat2mni_ants_workflow_nipype(SinkTag="anat_preproc",
wf_name="anat2mni_ants"):
"""
Register skull and brain extracted image to MNI space and return the transformation martices.
Using ANTS, doing it in the nipype way.
Workflow inputs:
:param skull: The reoriented anatomical file.
:param brain: The brain extracted anat.
:param ref_skull: MNI152 skull file.
:param ref_brain: MNI152 brain file.
:param SinkDir:
:param SinkTag: The output directiry in which the returned images (see workflow outputs) could be found.
Workflow outputs:
:return: anat2mni_workflow - workflow
anat="/home/balint/Dokumentumok/phd/essen/PAINTER/probe/MS001/highres.nii.gz",
brain="/home/balint/Dokumentumok/phd/essen/PAINTER/probe/MS001/highres_brain.nii.gz",
Tamas Spisak
[email protected]
2018
"""
SinkDir = os.path.abspath(globals._SinkDir_ + "/" + SinkTag)
if not os.path.exists(SinkDir):
os.makedirs(SinkDir)
# Define inputs of workflow
inputspec = pe.Node(utility.IdentityInterface(
fields=['brain', 'skull', 'reference_brain', 'reference_skull']),
name='inputspec')
inputspec.inputs.reference_brain = globals._FSLDIR_ + globals._brainref #TODO_ready: 1 or 2mm???
inputspec.inputs.reference_skull = globals._FSLDIR_ + globals._headref
# Multi-stage registration node with ANTS
reg = pe.MapNode(
interface=Registration(),
iterfield=['moving_image'], # 'moving_image_mask'],
name="ANTS")
"""
reg.inputs.transforms = ['Affine', 'SyN']
reg.inputs.transform_parameters = [(2.0,), (0.1, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[1500, 200], [100, 50, 30]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.initialize_transforms_per_stage = False
reg.inputs.metric = ['Mattes', 'Mattes']
reg.inputs.metric_weight = [1] * 2 # Default (value ignored currently by ANTs)
reg.inputs.radius_or_number_of_bins = [32] * 2
reg.inputs.sampling_strategy = ['Random', None]
reg.inputs.sampling_percentage = [0.05, None]
reg.inputs.convergence_threshold = [1.e-8, 1.e-9]
reg.inputs.convergence_window_size = [20] * 2
reg.inputs.smoothing_sigmas = [[1, 0], [2, 1, 0]]
reg.inputs.sigma_units = ['vox'] * 2
reg.inputs.shrink_factors = [[2, 1], [4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True, True]
reg.inputs.use_histogram_matching = [True, True] # This is the default
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.winsorize_lower_quantile = 0.01
reg.inputs.winsorize_upper_quantile = 0.99
"""
#satra says:
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = ([[10000, 111110, 11110]] * 2 +
[[100, 50, 30]])
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.args = '--float'
# Create png images for quality check
myqc = qc.vol2png("anat2mni", "ANTS3", overlayiterated=False)
myqc.inputs.inputspec.overlay_image = globals._FSLDIR_ + globals._brainref #TODO_ready: 1 or 2mm???
myqc.inputs.slicer.image_width = 500 # 5000 # for the 1mm template
myqc.inputs.slicer.threshold_edges = 0.1 # 0.1 # for the 1mm template
# Save outputs which are important
ds = pe.Node(interface=io.DataSink(), name='ds_nii')
ds.inputs.base_directory = SinkDir
ds.inputs.regexp_substitutions = [("(\/)[^\/]*$", ".nii.gz")]
# Define outputs of the workflow
outputspec = pe.Node(utility.IdentityInterface(fields=[
'output_brain', 'linear_xfm', 'invlinear_xfm', 'nonlinear_xfm',
'invnonlinear_xfm', 'std_template'
]),
name='outputspec')
outputspec.inputs.std_template = inputspec.inputs.reference_brain
# Create workflow nad connect nodes
analysisflow = pe.Workflow(name=wf_name)
analysisflow.connect(inputspec, 'reference_skull', reg, 'fixed_image')
#analysisflow.connect(inputspec, 'reference_brain', reg, 'fixed_image_mask')
analysisflow.connect(inputspec, 'skull', reg, 'moving_image')
#analysisflow.connect(inputspec, 'brain', reg, 'moving_image_mask')
analysisflow.connect(reg, 'composite_transform', outputspec,
'nonlinear_xfm')
analysisflow.connect(reg, 'inverse_composite_transform', outputspec,
'invnonlinear_xfm')
analysisflow.connect(reg, 'warped_image', outputspec, 'output_brain')
analysisflow.connect(reg, 'warped_image', ds, 'anat2mni_std')
analysisflow.connect(reg, 'composite_transform', ds, 'anat2mni_warpfield')
analysisflow.connect(reg, 'warped_image', myqc, 'inputspec.bg_image')
return analysisflow
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 distortion_correction_workflow(self):
# The initial script created by Vinit Srivastava was:
# antsIntermodalityIntrasubject.sh -d 3 -i eddy_corr_brain_b0.nii.gz -r
# T1-nonGdE_brain_N4bfc_masked.nii.gz -x T1-nonGdE_brain_mask.nii.gz -w
# template -o B0toT1SmallWarp -t 2
#
# Note: the script antsIntermodalityIntrasubject.sh returns an error regarding a missing template file:
# template1Warp.nii.gz does not exist - please specify in order to proceed to steps that map to the template
# This is expected and means that the second half of the script is not executed nor necessary for this step.
# https://github.com/ANTsX/ANTs/blob/master/Scripts/antsIntermodalityIntrasubject.sh
#
# Additionally, the anatomical T1 brain mask is used in the second part of the script and is not useful in our
# case.
#
# The ants interface from nipype doesn't wrap the antsIntermodalityIntrasubject.sh script
#
# antsIntermodalityIntrasubject.sh Script breakdown:
# Usage: `basename $0`
# -d imageDimension
# -r anatomicalT1image(brain or whole - head, depending on modality) to align to
# -R anatomicalReference image to warp to(often higher resolution thananatomicalT1image)
# -i scalarImageToMatch(such as avgerage bold, averge dwi, etc.)
# -x anatomicalT1brainmask(should mask out regions that do not appear in scalarImageToMatch)
# -t transformType(0 = rigid, 1 = affine, 2 = rigid + small_def, 3 = affine + small_def)
# -w prefix of T1 to template transform
# -T template space
# < OPTARGS >
# -o outputPrefix
# -l labels in template space
# -a auxiliary scalar image/s to warp to template
# -b auxiliary dt image to warp to template
# Initial command runs:
# /opt/ants-2.3.1/antsRegistration -d 3 -m MI[anatomicalImage(-r), scalarImage(-i),1,32,Regular,0.25]
# -c [1000x500x250x0,1e-7,5] -t Rigid[0.1] -f 8x4x2x1 -s 4x2x1x0
# -u 1 -m mattes[anatomicalImage(-r), scalarImage(-i),1,32] -c [50x50x0,1e-7,5] -t SyN[0.1,3,0] -f 4x2x1
# -s 2x1x0mm -u 1 -z 1 --winsorize-image-intensities [0.005, 0.995] -o B0toT1Warp
# -d: dimensionality
# -m: metric
# "MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]" );
# "Mattes[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]" );
# -c: convergence
# "MxNxO"
# "[MxNxO,<convergenceThreshold=1e-6>,<convergenceWindowSize=10>]"
# -t: transform
# 0:rigid[GradientStep], 1:affine[], 2:composite affine[], 3:similarity[], 4:translation[], 5:BSpline[]
# "SyN[gradientStep,<updateFieldVarianceInVoxelSpace=3>,<totalFieldVarianceInVoxelSpace=0>]"
# -f: shrink-factors
# "MxNxO..."
# -s: smoothing-sigmas
# "MxNxO..."
# -u: use-histogram-matching
# -z: collapse-output-transforms
# -o: output transform prefix
b0_T1w_Reg = Node(Registration(), name="b0_T1w_Reg")
b0_T1w_Reg.btn_string = 'dwi b0 to T1w Registration'
# -r, -i, -x will get set via workflow implementation
# -d
b0_T1w_Reg.inputs.dimension = 3
# -m
b0_T1w_Reg.inputs.metric = ['MI', 'Mattes']
b0_T1w_Reg.inputs.metric_weight = [1, 1]
b0_T1w_Reg.inputs.radius_or_number_of_bins = [32, 32]
b0_T1w_Reg.inputs.sampling_strategy = ['Regular', None]
b0_T1w_Reg.inputs.sampling_percentage = [0.25, None]
# -c
b0_T1w_Reg.inputs.number_of_iterations = [[1000, 500, 250, 0],
[50, 50, 0]]
b0_T1w_Reg.inputs.convergence_threshold = [1e-7, 1e-7]
b0_T1w_Reg.inputs.convergence_window_size = [5, 5]
# -t
b0_T1w_Reg.inputs.transforms = ['Rigid', 'SyN']
b0_T1w_Reg.inputs.transform_parameters = [(0.1, ), (0.1, 3, 0.0)]
# -f
b0_T1w_Reg.inputs.shrink_factors = [[8, 4, 2, 1], [4, 2, 1]]
# -s
b0_T1w_Reg.inputs.smoothing_sigmas = [[4, 2, 1, 0], [2, 1, 0]]
b0_T1w_Reg.inputs.sigma_units = ['vox', 'mm']
# -u
b0_T1w_Reg.inputs.use_histogram_matching = [True, True]
# -z
b0_T1w_Reg.inputs.collapse_output_transforms = True
# winsorize
b0_T1w_Reg.inputs.winsorize_lower_quantile = 0.005
b0_T1w_Reg.inputs.winsorize_upper_quantile = 0.995
# -o
b0_T1w_Reg.inputs.output_transform_prefix = 'dwiToT1Warp'
# Since the antsApplyTransform interface in nipype only accepts the transform list in the reverse order (i.e.
# the output from the antsRegistration script needs to be flipped) we save the transform files as a single
# composite file.
b0_T1w_Reg.inputs.write_composite_transform = True
self.interfaces.append(b0_T1w_Reg)
# Althought the antsRegistration interface can output a warped image, we keep the antsApplyTransform node to
# replicate the original (i.e. not nipype) pipeline and to add the input_image_type parameter.\
# second script: antsApplyTransforms
# antsApplyTransforms -d 3 -e 3 -i data.nii.gz -o data_distcorr.nii.gz -r
# eddy_corr_brain_b0.nii.gz -t B0toT1SmallWarp1Warp.nii.gz -t
# B0toT1SmallWarp0GenericAffine.mat -v
dwi_T1w_Tran = Node(ApplyTransforms(), name="dwi_T1w_Tran")
dwi_T1w_Tran.btn_string = 'dwi to T1w Transformation'
# -d: dimension
dwi_T1w_Tran.inputs.dimension = 3
# -e: input image type
dwi_T1w_Tran.inputs.input_image_type = 3
# the -i, -o, -r, -t options are from workflow
self.interfaces.append(dwi_T1w_Tran)
def prep_for_fmriprep(bidsdir, rawdir, substr):
#make subject dir, anat and func
subid = substr.replace('-', '_').replace('_', '')
anatdir = bidsdir + '/sub-' + subid + '/anat/'
funcdir = bidsdir + '/sub-' + subid + '/func/'
os.makedirs(anatdir, exist_ok=True)
os.makedirs(funcdir, exist_ok=True)
# get t1brain and MNI template
t1brain = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/highres.nii.gz' % substr
template = str(
get_template('MNI152NLin2009cAsym',
resolution=2,
desc='brain',
suffix='T1w',
extension=['.nii', '.nii.gz']))
## registered T1w to template for fmriprep standard
### this reg files may not be used
tranformfile = tempfile.mkdtemp()
reg = Registration()
reg.inputs.fixed_image = template
reg.inputs.moving_image = t1brain
reg.inputs.output_transform_prefix = tranformfile + '/t12mni_'
reg.inputs.transforms = ['Affine', 'SyN']
reg.inputs.transform_parameters = [(2.0, ), (0.25, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[1500, 200], [100, 50, 30]]
reg.inputs.dimension = 3
reg.inputs.num_threads = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.initialize_transforms_per_stage = True
reg.inputs.metric = ['Mattes'] * 2
reg.inputs.metric_weight = [
1
] * 2 # Default (value ignored currently by ANTs)
reg.inputs.radius_or_number_of_bins = [32] * 2
reg.inputs.sampling_strategy = ['Random', None]
reg.inputs.sampling_percentage = [0.05, None]
reg.inputs.convergence_threshold = [1.e-8, 1.e-9]
reg.inputs.convergence_window_size = [20] * 2
reg.inputs.smoothing_sigmas = [[1, 0], [2, 1, 0]]
reg.inputs.sigma_units = ['vox'] * 2
reg.inputs.shrink_factors = [[2, 1], [3, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True, True]
reg.inputs.use_histogram_matching = [True, True] # This is the default
#reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.cmdline
reg.run()
## copy transform file to fmriprep directory
mni2twtransform = anatdir + '/sub-' + subid + '_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5'
t1w2mnitransform = anatdir + '/sub-' + subid + '_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5'
copyfile(tranformfile + '/t12mni_Composite.h5', t1w2mnitransform)
copyfile(tranformfile + '/t12mni_InverseComposite.h5', mni2twtransform)
### warp the non-processed/filtered/smooth bold to fmriprep
### now functional
boldmask = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/mask.nii.gz' % substr
boldref = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI_SBREF.nii.gz' % substr
boldprep = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.nii.gz' % substr
reffile = tempfile.mkdtemp() + '/reffile.nii.gz'
boldstd = reffile = tempfile.mkdtemp() + '/boldstd.nii.gz'
maskstd = reffile = tempfile.mkdtemp() + '/maskstd.nii.gz'
aw = fsl.ApplyWarp()
aw.inputs.in_file = boldref
aw.inputs.ref_file = template
aw.inputs.field_file = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/example_func2standard_warp.nii.gz' % substr
aw.inputs.out_file = reffile
aw.inputs.output_type = 'NIFTI_GZ'
res = aw.run()
aw1 = fsl.ApplyWarp()
aw1.inputs.interp = 'spline'
aw1.inputs.ref_file = template
aw1.inputs.field_file = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/example_func2standard_warp.nii.gz' % substr
aw1.inputs.in_file = boldprep
aw1.inputs.out_file = boldstd
aw1.inputs.output_type = 'NIFTI_GZ'
res1 = aw1.run()
aw2 = fsl.ApplyWarp()
aw2.inputs.in_file = boldmask
aw2.inputs.ref_file = template
aw2.inputs.field_file = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/example_func2standard_warp.nii.gz' % substr
aw2.inputs.out_file = maskstd
aw2.inputs.output_type = 'NIFTI_GZ'
res2 = aw2.run()
tr = nb.load(boldprep).header.get_zooms()[-1]
jsontis = {
"RepetitionTime": np.float64(tr),
"TaskName": 'rest',
"SkullStripped": False,
}
jsmaks = {"mask": True}
#newname
preprocbold = funcdir + '/sub-' + subid + '_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
preprocboldjson = funcdir + '/sub-' + subid + '_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.json'
preprocboldref = funcdir + '/sub-' + subid + '_task-rest_space-MNI152NLin2009cAsym_boldref.nii.gz'
preprocmask = funcdir + '/sub-' + subid + '_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz'
preprocmaskjson = funcdir + '/sub-' + subid + '_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.json'
copyfile(maskstd, preprocmask)
copyfile(reffile, preprocboldref)
copyfile(boldstd, preprocbold)
writejson(jsontis, preprocboldjson)
writejson(jsmaks, preprocmaskjson)
# get wm and csf mask to extract mean signals for regressors
### first warp the anatomical to bold space
wmask = rawdir + '/UKB_Pipeline/%s/T1/T1_fast/T1_brain_pve_2.nii.gz' % substr
csfmask = rawdir + '/UKB_Pipeline/%s/T1/T1_fast/T1_brain_pve_0.nii.gz' % substr
t2funcwmask = tempfile.mkdtemp() + '/wmask.nii.gz'
t2funcwcsf = tempfile.mkdtemp() + '/csf.nii.gz'
aw = fsl.preprocess.ApplyXFM()
aw.inputs.in_file = wmask
aw.inputs.reference = boldref
aw.inputs.in_matrix_file = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/highres2example_func.mat' % substr
aw.inputs.out_file = t2funcwmask
aw.inputs.apply_xfm = True
aw.inputs.interp = 'nearestneighbour'
aw.inputs.output_type = 'NIFTI_GZ'
res = aw.run()
aw2 = fsl.preprocess.ApplyXFM()
aw2.inputs.in_file = csfmask
aw2.inputs.reference = boldref
aw2.inputs.in_matrix_file = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/reg/highres2example_func.mat' % substr
aw2.inputs.out_file = t2funcwcsf
aw2.inputs.apply_xfm = True
aw2.inputs.interp = 'nearestneighbour'
aw2.inputs.output_type = 'NIFTI_GZ'
res2 = aw2.run()
# binarized and extract signals
wmbin = nb.load(t2funcwmask).get_fdata()
wmbin[wmbin < 0.99999] = 0
csfbin = nb.load(t2funcwcsf).get_fdata()
csfbin[csfbin < 0.99999] = 0
maskbin = nb.load(boldmask).get_fdata()
bolddata = nb.load(boldprep).get_fdata()
wm_mean = bolddata[wmbin > 0, :].mean(axis=0)
csf_mean = bolddata[csfbin > 0, :].mean(axis=0)
global_mean = bolddata[maskbin > 0, :].mean(axis=0)
#### combine all the regressors
mcfile = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/mc/prefiltered_func_data_mcf.par' % substr
rsmdfile = rawdir + '/UKB_Pipeline/%s/fMRI_nosmooth/rfMRI.ica/mc/prefiltered_func_data_mcf_abs.rms' % substr
motionfile = np.loadtxt(mcfile)
rsmd = np.loadtxt(rsmdfile)
motionparam = pd.DataFrame(
motionfile,
columns=['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z'])
otherparam = pd.DataFrame({
'global_signal': global_mean,
'white_matter': wm_mean,
'csf': csf_mean,
'rmsd': rsmd
})
regressors = pd.concat([motionparam, otherparam], axis=1)
jsonreg = {'regressor': 'not'}
regcsv = funcdir + '/sub-' + subid + '_task-rest_desc-confounds_timeseries.tsv'
regjson = funcdir + '/sub-' + subid + '_task-rest_desc-confounds_timeseries.json'
regressors.to_csv(regcsv, index=None, sep='\t')
writejson(jsonreg, regjson)
def mk_w_angio(freesurfer_dir, angiogram, out_dir):
n_input = Node(IdentityInterface(fields=[
'fs_dir',
'fs_subj',
'angiogram',
'out_dir',
]),
name='input')
n_input.inputs.fs_dir = str(freesurfer_dir.parent)
n_input.inputs.fs_subj = freesurfer_dir.name
n_input.inputs.angiogram = str(angiogram)
n_input.inputs.out_dir = str(out_dir)
n_coreg = Node(Registration(), name='antsReg')
n_coreg.inputs.num_threads = 40
n_coreg.inputs.use_histogram_matching = False
n_coreg.inputs.dimension = 3
n_coreg.inputs.winsorize_lower_quantile = 0.001
n_coreg.inputs.winsorize_upper_quantile = 0.999
n_coreg.inputs.float = True
n_coreg.inputs.interpolation = 'Linear'
n_coreg.inputs.transforms = [
'Rigid',
]
n_coreg.inputs.transform_parameters = [
[
0.1,
],
]
n_coreg.inputs.metric = [
'MI',
]
n_coreg.inputs.metric_weight = [
1,
]
n_coreg.inputs.radius_or_number_of_bins = [
32,
]
n_coreg.inputs.sampling_strategy = [
'Regular',
]
n_coreg.inputs.sampling_percentage = [
0.5,
]
n_coreg.inputs.sigma_units = [
'mm',
]
n_coreg.inputs.convergence_threshold = [
1e-6,
]
n_coreg.inputs.smoothing_sigmas = [
[1, 0],
]
n_coreg.inputs.shrink_factors = [
[1, 1],
]
n_coreg.inputs.convergence_window_size = [
10,
]
n_coreg.inputs.number_of_iterations = [
[250, 100],
]
n_coreg.inputs.output_warped_image = True
n_coreg.inputs.output_inverse_warped_image = True
n_coreg.inputs.output_transform_prefix = 'angio_to_struct'
n_apply = Node(ApplyTransforms(), name='ants_apply')
n_apply.inputs.dimension = 3
n_apply.inputs.interpolation = 'Linear'
n_apply.inputs.default_value = 0
n_convert = Node(MRIConvert(), 'convert')
n_convert.inputs.out_type = 'niigz'
n_binarize = Node(Threshold(), 'make_mask')
n_binarize.inputs.thresh = .1
n_binarize.inputs.args = '-bin'
n_mask = Node(BinaryMaths(), 'mask')
n_mask.inputs.operation = 'mul'
n_veins = Node(Rename(), 'rename_veins')
n_veins.inputs.format_string = 'angiogram.nii.gz'
n_sink = Node(DataSink(), 'sink')
n_sink.inputs.base_directory = '/Fridge/users/giovanni/projects/intraop/loenen/angiogram'
n_sink.inputs.remove_dest_dir = True
fs = Node(FreeSurferSource(), 'freesurfer')
n_split = Node(Split(), 'split_pca')
n_split.inputs.dimension = 't'
w = Workflow('tmp_angiogram')
w.base_dir = str(out_dir)
w.connect(n_input, 'fs_dir', fs, 'subjects_dir')
w.connect(n_input, 'fs_subj', fs, 'subject_id')
w.connect(n_input, 'angiogram', n_split, 'in_file')
w.connect(n_split, ('out_files', select_file, 0), n_coreg, 'moving_image')
w.connect(fs, 'T1', n_coreg, 'fixed_image')
w.connect(n_coreg, 'forward_transforms', n_apply, 'transforms')
w.connect(n_split, ('out_files', select_file, 1), n_apply, 'input_image')
w.connect(fs, 'T1', n_apply, 'reference_image')
w.connect(fs, 'brain', n_convert, 'in_file')
w.connect(n_convert, 'out_file', n_binarize, 'in_file')
w.connect(n_apply, 'output_image', n_mask, 'in_file')
w.connect(n_binarize, 'out_file', n_mask, 'operand_file')
w.connect(n_mask, 'out_file', n_veins, 'in_file')
w.connect(n_input, 'out_dir', n_sink, 'base_directory')
w.connect(n_veins, 'out_file', n_sink, '@angiogram')
w.connect(n_convert, 'out_file', n_sink, '@brain')
return w
def builder(subject_id,
subId,
project_dir,
data_dir,
output_dir,
output_final_dir,
output_interm_dir,
layout,
anat=None,
funcs=None,
fmaps=None,
task_name='',
session=None,
apply_trim=False,
apply_dist_corr=False,
apply_smooth=False,
apply_filter=False,
mni_template='2mm',
apply_n4=True,
ants_threads=8,
readable_crash_files=False,
write_logs=True):
"""
Core function that returns a workflow. See wfmaker for more details.
Args:
subject_id: name of subject folder for final outputted sub-folder name
subId: abbreviate name of subject for intermediate outputted sub-folder name
project_dir: full path to root of project
data_dir: full path to raw data files
output_dir: upper level output dir (others will be nested within this)
output_final_dir: final preprocessed sub-dir name
output_interm_dir: intermediate preprcess sub-dir name
layout: BIDS layout instance
"""
##################
### PATH SETUP ###
##################
if session is not None:
session = int(session)
if session < 10:
session = '0' + str(session)
else:
session = str(session)
# Set MNI template
MNItemplate = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain.nii.gz')
MNImask = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain_mask.nii.gz')
MNItemplatehasskull = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '.nii.gz')
# Set ANTs files
bet_ants_template = os.path.join(get_resource_path(),
'OASIS_template.nii.gz')
bet_ants_prob_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumProbabilityMask.nii.gz')
bet_ants_registration_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumRegistrationMask.nii.gz')
#################################
### NIPYPE IMPORTS AND CONFIG ###
#################################
# Update nipype global config because workflow.config[] = ..., doesn't seem to work
# Can't store nipype config/rc file in container anyway so set them globaly before importing and setting up workflow as suggested here: http://nipype.readthedocs.io/en/latest/users/config_file.html#config-file
# Create subject's intermediate directory before configuring nipype and the workflow because that's where we'll save log files in addition to intermediate files
if not os.path.exists(os.path.join(output_interm_dir, subId, 'logs')):
os.makedirs(os.path.join(output_interm_dir, subId, 'logs'))
log_dir = os.path.join(output_interm_dir, subId, 'logs')
from nipype import config
if readable_crash_files:
cfg = dict(execution={'crashfile_format': 'txt'})
config.update_config(cfg)
config.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': write_logs
},
'execution': {
'crashdump_dir': log_dir
}
})
from nipype import logging
logging.update_logging(config)
# Now import everything else
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.nipy.preprocess import ComputeMask
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces.ants.segmentation import BrainExtraction, N4BiasFieldCorrection
from nipype.interfaces.ants import Registration, ApplyTransforms
from nipype.interfaces.fsl import MCFLIRT, TOPUP, ApplyTOPUP
from nipype.interfaces.fsl.maths import MeanImage
from nipype.interfaces.fsl import Merge as MERGE
from nipype.interfaces.fsl.utils import Smooth
from nipype.interfaces.nipy.preprocess import Trim
from .interfaces import Plot_Coregistration_Montage, Plot_Quality_Control, Plot_Realignment_Parameters, Create_Covariates, Down_Sample_Precision, Create_Encoding_File, Filter_In_Mask
##################
### INPUT NODE ###
##################
# Turn functional file list into interable Node
func_scans = Node(IdentityInterface(fields=['scan']), name='func_scans')
func_scans.iterables = ('scan', funcs)
# Get TR for use in filtering below; we're assuming all BOLD runs have the same TR
tr_length = layout.get_metadata(funcs[0])['RepetitionTime']
#####################################
## TRIM ##
#####################################
if apply_trim:
trim = Node(Trim(), name='trim')
trim.inputs.begin_index = apply_trim
#####################################
## DISTORTION CORRECTION ##
#####################################
if apply_dist_corr:
# Get fmap file locations
fmaps = [
f.filename for f in layout.get(
subject=subId, modality='fmap', extensions='.nii.gz')
]
if not fmaps:
raise IOError(
"Distortion Correction requested but field map scans not found..."
)
# Get fmap metadata
totalReadoutTimes, measurements, fmap_pes = [], [], []
for i, fmap in enumerate(fmaps):
# Grab total readout time for each fmap
totalReadoutTimes.append(
layout.get_metadata(fmap)['TotalReadoutTime'])
# Grab measurements (for some reason pyBIDS doesn't grab dcm_meta... fields from side-car json file and json.load, doesn't either; so instead just read the header using nibabel to determine number of scans)
measurements.append(nib.load(fmap).header['dim'][4])
# Get phase encoding direction
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
fmap_pes.append(fmap_pe)
encoding_file_writer = Node(interface=Create_Encoding_File(),
name='create_encoding')
encoding_file_writer.inputs.totalReadoutTimes = totalReadoutTimes
encoding_file_writer.inputs.fmaps = fmaps
encoding_file_writer.inputs.fmap_pes = fmap_pes
encoding_file_writer.inputs.measurements = measurements
encoding_file_writer.inputs.file_name = 'encoding_file.txt'
merge_to_file_list = Node(interface=Merge(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
merge_to_file_list.inputs.in1 = fmaps[0]
merge_to_file_list.inputs.in1 = fmaps[1]
# Merge AP and PA distortion correction scans
merger = Node(interface=MERGE(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.in_files = fmaps
merger.inputs.merged_file = 'merged_epi.nii.gz'
# Create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.output_type = 'NIFTI_GZ'
# Apply distortion correction to other scans
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.output_type = 'NIFTI_GZ'
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
###################################
### REALIGN ###
###################################
realign_fsl = Node(MCFLIRT(), name="realign")
realign_fsl.inputs.cost = 'mutualinfo'
realign_fsl.inputs.mean_vol = True
realign_fsl.inputs.output_type = 'NIFTI_GZ'
realign_fsl.inputs.save_mats = True
realign_fsl.inputs.save_rms = True
realign_fsl.inputs.save_plots = True
###################################
### MEAN EPIs ###
###################################
# For coregistration after realignment
mean_epi = Node(MeanImage(), name='mean_epi')
mean_epi.inputs.dimension = 'T'
# For after normalization is done to plot checks
mean_norm_epi = Node(MeanImage(), name='mean_norm_epi')
mean_norm_epi.inputs.dimension = 'T'
###################################
### MASK, ART, COV CREATION ###
###################################
compute_mask = Node(ComputeMask(), name='compute_mask')
compute_mask.inputs.m = .05
art = Node(ArtifactDetect(), name='art')
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'FSL'
make_cov = Node(Create_Covariates(), name='make_cov')
################################
### N4 BIAS FIELD CORRECTION ###
################################
if apply_n4:
n4_correction = Node(N4BiasFieldCorrection(), name='n4_correction')
n4_correction.inputs.copy_header = True
n4_correction.inputs.save_bias = False
n4_correction.inputs.num_threads = ants_threads
n4_correction.inputs.input_image = anat
###################################
### BRAIN EXTRACTION ###
###################################
brain_extraction_ants = Node(BrainExtraction(), name='brain_extraction')
brain_extraction_ants.inputs.dimension = 3
brain_extraction_ants.inputs.use_floatingpoint_precision = 1
brain_extraction_ants.inputs.num_threads = ants_threads
brain_extraction_ants.inputs.brain_probability_mask = bet_ants_prob_mask
brain_extraction_ants.inputs.keep_temporary_files = 1
brain_extraction_ants.inputs.brain_template = bet_ants_template
brain_extraction_ants.inputs.extraction_registration_mask = bet_ants_registration_mask
brain_extraction_ants.inputs.out_prefix = 'bet'
###################################
### COREGISTRATION ###
###################################
coregistration = Node(Registration(), name='coregistration')
coregistration.inputs.float = False
coregistration.inputs.output_transform_prefix = "meanEpi2highres"
coregistration.inputs.transforms = ['Rigid']
coregistration.inputs.transform_parameters = [(0.1, ), (0.1, )]
coregistration.inputs.number_of_iterations = [[1000, 500, 250, 100]]
coregistration.inputs.dimension = 3
coregistration.inputs.num_threads = ants_threads
coregistration.inputs.write_composite_transform = True
coregistration.inputs.collapse_output_transforms = True
coregistration.inputs.metric = ['MI']
coregistration.inputs.metric_weight = [1]
coregistration.inputs.radius_or_number_of_bins = [32]
coregistration.inputs.sampling_strategy = ['Regular']
coregistration.inputs.sampling_percentage = [0.25]
coregistration.inputs.convergence_threshold = [1e-08]
coregistration.inputs.convergence_window_size = [10]
coregistration.inputs.smoothing_sigmas = [[3, 2, 1, 0]]
coregistration.inputs.sigma_units = ['mm']
coregistration.inputs.shrink_factors = [[4, 3, 2, 1]]
coregistration.inputs.use_estimate_learning_rate_once = [True]
coregistration.inputs.use_histogram_matching = [False]
coregistration.inputs.initial_moving_transform_com = True
coregistration.inputs.output_warped_image = True
coregistration.inputs.winsorize_lower_quantile = 0.01
coregistration.inputs.winsorize_upper_quantile = 0.99
###################################
### NORMALIZATION ###
###################################
# Settings Explanations
# Only a few key settings are worth adjusting and most others relate to how ANTs optimizer starts or iterates and won't make a ton of difference
# Brian Avants referred to these settings as the last "best tested" when he was aligning fMRI data: https://github.com/ANTsX/ANTsRCore/blob/master/R/antsRegistration.R#L275
# Things that matter the most:
# smoothing_sigmas:
# how much gaussian smoothing to apply when performing registration, probably want the upper limit of this to match the resolution that the data is collected at e.g. 3mm
# Old settings [[3,2,1,0]]*3
# shrink_factors
# The coarseness with which to do registration
# Old settings [[8,4,2,1]] * 3
# >= 8 may result is some problems causing big chunks of cortex with little fine grain spatial structure to be moved to other parts of cortex
# Other settings
# transform_parameters:
# how much regularization to do for fitting that transformation
# for syn this pertains to both the gradient regularization term, and the flow, and elastic terms. Leave the syn settings alone as they seem to be the most well tested across published data sets
# radius_or_number_of_bins
# This is the bin size for MI metrics and 32 is probably adequate for most use cases. Increasing this might increase precision (e.g. to 64) but takes exponentially longer
# use_histogram_matching
# Use image intensity distribution to guide registration
# Leave it on for within modality registration (e.g. T1 -> MNI), but off for between modality registration (e.g. EPI -> T1)
# convergence_threshold
# threshold for optimizer
# convergence_window_size
# how many samples should optimizer average to compute threshold?
# sampling_strategy
# what strategy should ANTs use to initialize the transform. Regular here refers to approximately random sampling around the center of the image mass
normalization = Node(Registration(), name='normalization')
normalization.inputs.float = False
normalization.inputs.collapse_output_transforms = True
normalization.inputs.convergence_threshold = [1e-06]
normalization.inputs.convergence_window_size = [10]
normalization.inputs.dimension = 3
normalization.inputs.fixed_image = MNItemplate
normalization.inputs.initial_moving_transform_com = True
normalization.inputs.metric = ['MI', 'MI', 'CC']
normalization.inputs.metric_weight = [1.0] * 3
normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
[1000, 500, 250, 100],
[100, 70, 50, 20]]
normalization.inputs.num_threads = ants_threads
normalization.inputs.output_transform_prefix = 'anat2template'
normalization.inputs.output_inverse_warped_image = True
normalization.inputs.output_warped_image = True
normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
normalization.inputs.sampling_strategy = ['Regular', 'Regular', 'None']
normalization.inputs.shrink_factors = [[8, 4, 2, 1]] * 3
normalization.inputs.sigma_units = ['vox'] * 3
normalization.inputs.smoothing_sigmas = [[3, 2, 1, 0]] * 3
normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
normalization.inputs.transform_parameters = [(0.1, ), (0.1, ),
(0.1, 3.0, 0.0)]
normalization.inputs.use_histogram_matching = True
normalization.inputs.winsorize_lower_quantile = 0.005
normalization.inputs.winsorize_upper_quantile = 0.995
normalization.inputs.write_composite_transform = True
# NEW SETTINGS (need to be adjusted; specifically shink_factors and smoothing_sigmas need to be the same length)
# normalization = Node(Registration(), name='normalization')
# normalization.inputs.float = False
# normalization.inputs.collapse_output_transforms = True
# normalization.inputs.convergence_threshold = [1e-06, 1e-06, 1e-07]
# normalization.inputs.convergence_window_size = [10]
# normalization.inputs.dimension = 3
# normalization.inputs.fixed_image = MNItemplate
# normalization.inputs.initial_moving_transform_com = True
# normalization.inputs.metric = ['MI', 'MI', 'CC']
# normalization.inputs.metric_weight = [1.0]*3
# normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
# [1000, 500, 250, 100],
# [100, 70, 50, 20]]
# normalization.inputs.num_threads = ants_threads
# normalization.inputs.output_transform_prefix = 'anat2template'
# normalization.inputs.output_inverse_warped_image = True
# normalization.inputs.output_warped_image = True
# normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
# normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
# normalization.inputs.sampling_strategy = ['Regular',
# 'Regular',
# 'None']
# normalization.inputs.shrink_factors = [[4, 3, 2, 1]]*3
# normalization.inputs.sigma_units = ['vox']*3
# normalization.inputs.smoothing_sigmas = [[2, 1], [2, 1], [3, 2, 1, 0]]
# normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
# normalization.inputs.transform_parameters = [(0.1,),
# (0.1,),
# (0.1, 3.0, 0.0)]
# normalization.inputs.use_histogram_matching = True
# normalization.inputs.winsorize_lower_quantile = 0.005
# normalization.inputs.winsorize_upper_quantile = 0.995
# normalization.inputs.write_composite_transform = True
###################################
### APPLY TRANSFORMS AND SMOOTH ###
###################################
merge_transforms = Node(Merge(2),
iterfield=['in2'],
name='merge_transforms')
# Used for epi -> mni, via (coreg + norm)
apply_transforms = Node(ApplyTransforms(),
iterfield=['input_image'],
name='apply_transforms')
apply_transforms.inputs.input_image_type = 3
apply_transforms.inputs.float = False
apply_transforms.inputs.num_threads = 12
apply_transforms.inputs.environ = {}
apply_transforms.inputs.interpolation = 'BSpline'
apply_transforms.inputs.invert_transform_flags = [False, False]
apply_transforms.inputs.reference_image = MNItemplate
# Used for t1 segmented -> mni, via (norm)
apply_transform_seg = Node(ApplyTransforms(), name='apply_transform_seg')
apply_transform_seg.inputs.input_image_type = 3
apply_transform_seg.inputs.float = False
apply_transform_seg.inputs.num_threads = 12
apply_transform_seg.inputs.environ = {}
apply_transform_seg.inputs.interpolation = 'MultiLabel'
apply_transform_seg.inputs.invert_transform_flags = [False]
apply_transform_seg.inputs.reference_image = MNItemplate
###################################
### PLOTS ###
###################################
plot_realign = Node(Plot_Realignment_Parameters(), name="plot_realign")
plot_qa = Node(Plot_Quality_Control(), name="plot_qa")
plot_normalization_check = Node(Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = MNItemplatehasskull
############################################
### FILTER, SMOOTH, DOWNSAMPLE PRECISION ###
############################################
# Use cosanlab_preproc for down sampling
down_samp = Node(Down_Sample_Precision(), name="down_samp")
# Use FSL for smoothing
if apply_smooth:
smooth = Node(Smooth(), name='smooth')
if isinstance(apply_smooth, list):
smooth.iterables = ("fwhm", apply_smooth)
elif isinstance(apply_smooth, int) or isinstance(apply_smooth, float):
smooth.inputs.fwhm = apply_smooth
else:
raise ValueError("apply_smooth must be a list or int/float")
# Use cosanlab_preproc for low-pass filtering
if apply_filter:
lp_filter = Node(Filter_In_Mask(), name='lp_filter')
lp_filter.inputs.mask = MNImask
lp_filter.inputs.sampling_rate = tr_length
lp_filter.inputs.high_pass_cutoff = 0
if isinstance(apply_filter, list):
lp_filter.iterables = ("low_pass_cutoff", apply_filter)
elif isinstance(apply_filter, int) or isinstance(apply_filter, float):
lp_filter.inputs.low_pass_cutoff = apply_filter
else:
raise ValueError("apply_filter must be a list or int/float")
###################
### OUTPUT NODE ###
###################
# Collect all final outputs in the output dir and get rid of file name additions
datasink = Node(DataSink(), name='datasink')
if session:
datasink.inputs.base_directory = os.path.join(output_final_dir,
subject_id)
datasink.inputs.container = 'ses-' + session
else:
datasink.inputs.base_directory = output_final_dir
datasink.inputs.container = subject_id
# Remove substitutions
data_dir_parts = data_dir.split('/')[1:]
if session:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + [
'ses-' + session
] + ['func']
else:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + ['func']
func_scan_names = [os.path.split(elem)[-1] for elem in funcs]
to_replace = []
for elem in func_scan_names:
bold_name = elem.split(subject_id + '_')[-1]
bold_name = bold_name.split('.nii.gz')[0]
to_replace.append(('..'.join(prefix + [elem]), bold_name))
datasink.inputs.substitutions = to_replace
#####################
### INIT WORKFLOW ###
#####################
# If we have sessions provide the full path to the subject's intermediate directory
# and only rely on workflow init to create the session container *within* that directory
# Otherwise just point to the intermediate directory and let the workflow init create the subject container within the intermediate directory
if session:
workflow = Workflow(name='ses_' + session)
workflow.base_dir = os.path.join(output_interm_dir, subId)
else:
workflow = Workflow(name=subId)
workflow.base_dir = output_interm_dir
############################
######### PART (1a) #########
# func -> discorr -> trim -> realign
# OR
# func -> trim -> realign
# OR
# func -> discorr -> realign
# OR
# func -> realign
############################
if apply_dist_corr:
workflow.connect([(encoding_file_writer, topup, [('encoding_file',
'encoding_file')]),
(encoding_file_writer, apply_topup,
[('encoding_file', 'encoding_file')]),
(merger, topup, [('merged_file', 'in_file')]),
(func_scans, apply_topup, [('scan', 'in_files')]),
(topup, apply_topup,
[('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')])])
if apply_trim:
# Dist Corr + Trim
workflow.connect([(apply_topup, trim, [('out_corrected', 'in_file')
]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# Dist Corr + No Trim
workflow.connect([(apply_topup, realign_fsl, [('out_corrected',
'in_file')])])
else:
if apply_trim:
# No Dist Corr + Trim
workflow.connect([(func_scans, trim, [('scan', 'in_file')]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# No Dist Corr + No Trim
workflow.connect([
(func_scans, realign_fsl, [('scan', 'in_file')]),
])
############################
######### PART (1n) #########
# anat -> N4 -> bet
# OR
# anat -> bet
############################
if apply_n4:
workflow.connect([(n4_correction, brain_extraction_ants,
[('output_image', 'anatomical_image')])])
else:
brain_extraction_ants.inputs.anatomical_image = anat
##########################################
############### PART (2) #################
# realign -> coreg -> mni (via t1)
# t1 -> mni
# covariate creation
# plot creation
###########################################
workflow.connect([
(realign_fsl, plot_realign, [('par_file', 'realignment_parameters')]),
(realign_fsl, plot_qa, [('out_file', 'dat_img')]),
(realign_fsl, art, [('out_file', 'realigned_files'),
('par_file', 'realignment_parameters')]),
(realign_fsl, mean_epi, [('out_file', 'in_file')]),
(realign_fsl, make_cov, [('par_file', 'realignment_parameters')]),
(mean_epi, compute_mask, [('out_file', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(art, plot_realign, [('outlier_files', 'outliers')]),
(plot_qa, make_cov, [('fd_outliers', 'fd_outliers')]),
(brain_extraction_ants, coregistration, [('BrainExtractionBrain',
'fixed_image')]),
(mean_epi, coregistration, [('out_file', 'moving_image')]),
(brain_extraction_ants, normalization, [('BrainExtractionBrain',
'moving_image')]),
(coregistration, merge_transforms, [('composite_transform', 'in2')]),
(normalization, merge_transforms, [('composite_transform', 'in1')]),
(merge_transforms, apply_transforms, [('out', 'transforms')]),
(realign_fsl, apply_transforms, [('out_file', 'input_image')]),
(apply_transforms, mean_norm_epi, [('output_image', 'in_file')]),
(normalization, apply_transform_seg, [('composite_transform',
'transforms')]),
(brain_extraction_ants, apply_transform_seg,
[('BrainExtractionSegmentation', 'input_image')]),
(mean_norm_epi, plot_normalization_check, [('out_file', 'wra_img')])
])
##################################################
################### PART (3) #####################
# epi (in mni) -> filter -> smooth -> down sample
# OR
# epi (in mni) -> filter -> down sample
# OR
# epi (in mni) -> smooth -> down sample
# OR
# epi (in mni) -> down sample
###################################################
if apply_filter:
workflow.connect([(apply_transforms, lp_filter, [('output_image',
'in_file')])])
if apply_smooth:
# Filtering + Smoothing
workflow.connect([(lp_filter, smooth, [('out_file', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')
])])
else:
# Filtering + No Smoothing
workflow.connect([(lp_filter, down_samp, [('out_file', 'in_file')])
])
else:
if apply_smooth:
# No Filtering + Smoothing
workflow.connect([
(apply_transforms, smooth, [('output_image', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')])
])
else:
# No Filtering + No Smoothing
workflow.connect([(apply_transforms, down_samp, [('output_image',
'in_file')])])
##########################################
############### PART (4) #################
# down sample -> save
# plots -> save
# covs -> save
# t1 (in mni) -> save
# t1 segmented masks (in mni) -> save
# realignment parms -> save
##########################################
workflow.connect([
(down_samp, datasink, [('out_file', 'functional.@down_samp')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_qa, datasink, [('plot', 'functional.@plot_qa')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(normalization, datasink, [('warped_image', 'structural.@normanat')]),
(apply_transform_seg, datasink, [('output_image',
'structural.@normanatseg')]),
(realign_fsl, datasink, [('par_file', 'functional.@motionparams')])
])
if not os.path.exists(os.path.join(output_dir, 'pipeline.png')):
workflow.write_graph(dotfilename=os.path.join(output_dir, 'pipeline'),
format='png')
print(f"Creating workflow for subject: {subject_id}")
if ants_threads != 8:
print(
f"ANTs will utilize the user-requested {ants_threads} threads for parallel processing."
)
return workflow
# Registration - computes registration between subject's structural and MNI template.
antsreg = Node(Registration(args='--float',
collapse_output_transforms=True,
fixed_image=template,
initial_moving_transform_com=True,
num_threads=1,
output_inverse_warped_image=True,
output_warped_image=True,
sigma_units=['vox'] * 3,
transforms=['Rigid', 'Affine', 'SyN'],
terminal_output='file',
winsorize_lower_quantile=0.005,
winsorize_upper_quantile=0.995,
convergence_threshold=[1e-06],
convergence_window_size=[10],
metric=['MI', 'MI', 'CC'],
metric_weight=[1.0] * 3,
number_of_iterations=[[1000, 500, 250, 100],
[1000, 500, 250, 100],
[100, 70, 50, 20]],
radius_or_number_of_bins=[32, 32, 4],
sampling_percentage=[0.25, 0.25, 1],
sampling_strategy=['Regular', 'Regular', 'None'],
shrink_factors=[[8, 4, 2, 1]] * 3,
smoothing_sigmas=[[3, 2, 1, 0]] * 3,
transform_parameters=[(0.1, ), (0.1, ),
(0.1, 3.0, 0.0)],
use_histogram_matching=True,
write_composite_transform=True),
name='antsreg')
def registerToTemplate(fixedImgFn,
movingImgFn,
outFn,
outDir,
transformPrefix,
initialize=False,
initialRegFile=0,
regType='nonlinear'):
"""
Register 2 images taken at different timepoints.
Inputs:
- fixedImgFn: filename of the fixed image (should be the template image)
- movingImgFn: filename of the moving image (should be the Jn image)
- outFn: name of the file to write the transformed image to.
- outDir: path to the tmp directory
- transformPrefix: prefix for the transform function
- initialize: optional parameter to specify the location of the
transformation matrix from the previous registration
- initialRegFile: optional parameter to be used with the initialize paramter;
specifies which output_#Affine.mat file to use
- regType: optional parameter to specify the type of registration to use
(affine ['Affine'] or nonlinear ['SyN'])
Outputs:
- None
Effects:
- Saves the registered image and the registration files
"""
# Set up the registration
# For both Affine and SyN transforms
reg = Registration()
reg.inputs.fixed_image = fixedImgFn
reg.inputs.moving_image = movingImgFn
reg.inputs.output_transform_prefix = transformPrefix
reg.inputs.interpolation = 'NearestNeighbor'
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = False
reg.inputs.collapse_output_transforms = False
reg.inputs.initialize_transforms_per_stage = False
# Specify certain parameters for the nonlinear/['SyN'] registration
if regType == 'nonlinear':
reg.inputs.transforms = ['Affine', 'SyN']
reg.inputs.transform_parameters = [(2.0, ), (0.25, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[1500, 200], [100, 50, 30]]
reg.inputs.metric = ['CC'] * 2
reg.inputs.metric_weight = [1] * 2
reg.inputs.radius_or_number_of_bins = [5] * 2
reg.inputs.convergence_threshold = [1.e-8, 1.e-9]
reg.inputs.convergence_window_size = [20] * 2
reg.inputs.smoothing_sigmas = [[1, 0], [2, 1, 0]]
reg.inputs.sigma_units = ['vox'] * 2
reg.inputs.shrink_factors = [[2, 1], [3, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True, True]
reg.inputs.use_histogram_matching = [
True, True
] # This is the default value, but specify it anyway
# Specify certain parameters for the affine/['Affine'] registration
elif regType == 'affine':
reg.inputs.transforms = ['Affine']
reg.inputs.transform_parameters = [(2.0, )]
reg.inputs.number_of_iterations = [[1500, 200]]
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [5]
reg.inputs.convergence_threshold = [1.e-8]
reg.inputs.convergence_window_size = [20]
reg.inputs.smoothing_sigmas = [[1, 0]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [
True
] # This is the default, but specify it anyway
reg.inputs.output_warped_image = outFn
reg.inputs.num_threads = 50
# If the registration is initialized, set a few more parameters
if initialize is True:
reg.inputs.initial_moving_transform = transformPrefix + str(
initialRegFile) + 'Affine.mat'
reg.inputs.invert_initial_moving_transform = False
# Keep the user updated with the status of the registration
print("Starting", regType, "registration for", outFn)
# Run the registration
reg.run()
# Keep the user updated with the status of the registration
print("Finished", regType, "registration for", outFn)
def preproc_workflow(input_dir,
output_dir,
subject_list,
ses_list,
anat_file,
func_file,
scan_size=477,
bet_frac=0.37):
"""
The preprocessing workflow used in the preparation of the psilocybin vs escitalopram rsFMRI scans.
Workflows and notes are defined throughout. Inputs are designed to be general and masks/default MNI space is provided
:param input_dir: The input file directory containing all scans in BIDS format
:param output_dir: The output file directory
:param subject_list: a list of subject numbers
:param ses_list: a list of scan numbers (session numbers)
:param anat_file: The format of the anatomical scan within the input directory
:param func_file: The format of the functional scan within the input directory
:param scan_size: The length of the scan by number of images, most 10 minutes scans are around 400-500 depending
upon scanner defaults and parameters - confirm by looking at your data
:param bet_frac: brain extraction fractional intensity threshold
:return: the preprocessing workflow
"""
preproc = Workflow(name='preproc')
preproc.base_dir = output_dir
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'ses']),
name="infosource")
infosource.iterables = [('subject_id', subject_list), ('ses', ses_list)]
# SelectFiles - to grab the data (alternative to DataGrabber)
templates = {
'anat': anat_file,
'func': func_file
} # define the template of each file input
selectfiles = Node(SelectFiles(templates, base_directory=input_dir),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=output_dir, container=output_dir),
name="datasink")
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('ses', 'ses')])])
'''
This is your functional processing workflow, used to trim scans, despike the signal, slice-time correct,
and motion correct your data
'''
fproc = Workflow(name='fproc') # the functional processing workflow
# ExtractROI - skip dummy scans at the beginning of the recording by removing the first three
trim = Node(ExtractROI(t_min=3, t_size=scan_size, output_type='NIFTI_GZ'),
name="trim")
# 3dDespike - despike
despike = Node(Despike(outputtype='NIFTI_GZ', args='-NEW'), name="despike")
fproc.connect([(trim, despike, [('roi_file', 'in_file')])])
preproc.connect([(selectfiles, fproc, [('func', 'trim.in_file')])])
# 3dTshift - slice time correction
slicetime = Node(TShift(outputtype='NIFTI_GZ', tpattern='alt+z2'),
name="slicetime")
fproc.connect([(despike, slicetime, [('out_file', 'in_file')])])
# 3dVolreg - correct motion and output 1d matrix
moco = Node(Volreg(outputtype='NIFTI_GZ',
interp='Fourier',
zpad=4,
args='-twopass'),
name="moco")
fproc.connect([(slicetime, moco, [('out_file', 'in_file')])])
moco_bpfdt = Node(
MOCObpfdt(), name='moco_bpfdt'
) # use the matlab function to correct the motion regressor
fproc.connect([(moco, moco_bpfdt, [('oned_file', 'in_file')])])
'''
This is the co-registration workflow using FSL and ANTs
'''
coreg = Workflow(name='coreg')
# BET - structural data brain extraction
bet_anat = Node(BET(output_type='NIFTI_GZ', frac=bet_frac, robust=True),
name="bet_anat")
# FSL segmentation process to get WM map
seg = Node(FAST(bias_iters=6,
img_type=1,
output_biascorrected=True,
output_type='NIFTI_GZ'),
name="seg")
coreg.connect([(bet_anat, seg, [('out_file', 'in_files')])])
# functional to structural registration
mean = Node(MCFLIRT(mean_vol=True, output_type='NIFTI_GZ'), name="mean")
# BBR using linear methods for initial transform fit
func2struc = Node(FLIRT(cost='bbr', dof=6, output_type='NIFTI_GZ'),
name='func2struc')
coreg.connect([(seg, func2struc, [('restored_image', 'reference')])])
coreg.connect([(mean, func2struc, [('mean_img', 'in_file')])])
coreg.connect([(seg, func2struc, [(('tissue_class_files', pickindex, 2),
'wm_seg')])])
# convert the FSL linear transform into a C3d format for AFNI
f2s_c3d = Node(C3dAffineTool(itk_transform=True, fsl2ras=True),
name='f2s_c3d')
coreg.connect([(func2struc, f2s_c3d, [('out_matrix_file', 'transform_file')
])])
coreg.connect([(mean, f2s_c3d, [('mean_img', 'source_file')])])
coreg.connect([(seg, f2s_c3d, [('restored_image', 'reference_file')])])
# Functional to structural registration via ANTs non-linear registration
reg = Node(Registration(
fixed_image='default_images/MNI152_T1_2mm_brain.nii.gz',
transforms=['Affine', 'SyN'],
transform_parameters=[(0.1, ), (0.1, 3.0, 0.0)],
number_of_iterations=[[1500, 1000, 1000], [100, 70, 50, 20]],
dimension=3,
write_composite_transform=True,
collapse_output_transforms=True,
metric=['MI'] + ['CC'],
metric_weight=[1] * 2,
radius_or_number_of_bins=[32] + [4],
convergence_threshold=[1.e-8, 1.e-9],
convergence_window_size=[20] + [10],
smoothing_sigmas=[[2, 1, 0], [4, 2, 1, 0]],
sigma_units=['vox'] * 2,
shrink_factors=[[4, 2, 1], [6, 4, 2, 1]],
use_histogram_matching=[False] + [True],
use_estimate_learning_rate_once=[True, True],
output_warped_image=True),
name='reg')
coreg.connect([(seg, reg, [('restored_image', 'moving_image')])
]) # connect segmentation node to registration node
merge1 = Node(niu.Merge(2), iterfield=['in2'],
name='merge1') # merge the linear and nonlinear transforms
coreg.connect([(f2s_c3d, merge1, [('itk_transform', 'in2')])])
coreg.connect([(reg, merge1, [('composite_transform', 'in1')])])
# warp the functional images into MNI space using the transforms from FLIRT and SYN
warp = Node(ApplyTransforms(
reference_image='default_images/MNI152_T1_2mm_brain.nii.gz',
input_image_type=3),
name='warp')
coreg.connect([(moco, warp, [('out_file', 'input_image')])])
coreg.connect([(merge1, warp, [('out', 'transforms')])])
preproc.connect([(selectfiles, coreg, [('anat', 'bet_anat.in_file')])])
preproc.connect([(fproc, coreg, [('moco.out_file', 'mean.in_file')])])
'''
Scrubbing workflow - find the motion outliers, bandpass filter, re-mean the data after bpf
'''
scrub = Workflow(name='scrub')
# Generate the Scrubbing Regressor
scrub_metrics = Node(MotionOutliers(dummy=4,
out_file='FD_outliers.1D',
metric='fd',
threshold=0.4),
name="scrub_metrics")
# regress out timepoints
scrub_frames = Node(Bandpass(highpass=0,
lowpass=99999,
outputtype='NIFTI_GZ'),
name='scrub_frames')
scrub.connect([(scrub_metrics, scrub_frames, [('out_file',
'orthogonalize_file')])])
preproc.connect([(coreg, scrub, [('warp.output_image',
'scrub_frames.in_file')])])
preproc.connect([(selectfiles, scrub, [('func', 'scrub_metrics.in_file')])
])
# mean image for remeaning after bandpass
premean = Node(TStat(args='-mean', outputtype='NIFTI_GZ'), name='premean')
# remean the image
remean2 = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean2')
scrub.connect([(scrub_frames, remean2, [('out_file', 'in_file_a')])])
scrub.connect([(premean, remean2, [('out_file', 'in_file_b')])])
preproc.connect([(coreg, scrub, [('warp.output_image', 'premean.in_file')])
])
'''
Regressors for final cleaning steps
'''
regressors = Workflow(name='regressors')
# Using registered structural image to create the masks for both WM and CSF
regbet = Node(BET(robust=True, frac=0.37, output_type='NIFTI_GZ'),
name='regbet')
regseg = Node(FAST(img_type=1,
output_type='NIFTI_GZ',
no_pve=True,
no_bias=True,
segments=True),
name='regseg')
regressors.connect([(regbet, regseg, [('out_file', 'in_files')])])
preproc.connect([(coreg, regressors, [('reg.warped_image',
'regbet.in_file')])])
'''
Create a cerebrospinal fluid (CSF) regressor
'''
# subtract subcortical GM from the CSF mask
subcortgm = Node(BinaryMaths(
operation='sub',
operand_file='default_images/subcortical_gm_mask_bin.nii.gz',
output_type='NIFTI_GZ',
args='-bin'),
name='subcortgm')
regressors.connect([(regseg, subcortgm, [(('tissue_class_files', pickindex,
0), 'in_file')])])
# Fill the mask holes
fillcsf = Node(MaskTool(fill_holes=True, outputtype='NIFTI_GZ'),
name='fillcsf')
regressors.connect([(subcortgm, fillcsf, [('out_file', 'in_file')])])
# Erode the mask
erocsf = Node(MaskTool(outputtype='NIFTI_GZ', dilate_inputs='-1'),
name='erocsf')
regressors.connect([(fillcsf, erocsf, [('out_file', 'in_file')])])
# Take mean csf signal from functional image
meancsf = Node(ImageMeants(output_type='NIFTI_GZ'), name='meancsf')
regressors.connect([(erocsf, meancsf, [('out_file', 'mask')])])
preproc.connect([(coreg, regressors, [('warp.output_image',
'meancsf.in_file')])])
bpf_dt_csf = Node(CSFbpfdt(), name='bpf_dt_csf')
regressors.connect([(meancsf, bpf_dt_csf, [('out_file', 'in_file')])])
'''
Creates a local white matter regressor
'''
# subtract subcortical gm
subcortgm2 = Node(BinaryMaths(
operation='sub',
operand_file='default_images/subcortical_gm_mask_bin.nii.gz',
output_type='NIFTI_GZ',
args='-bin'),
name='subcortgm2')
regressors.connect([(regseg, subcortgm2, [(('tissue_class_files',
pickindex, 2), 'in_file')])])
# fill mask
fillwm = Node(MaskTool(fill_holes=True, outputtype='NIFTI_GZ'),
name='fillwm')
regressors.connect([(subcortgm2, fillwm, [('out_file', 'in_file')])])
# erod mask
erowm = Node(MaskTool(outputtype='NIFTI_GZ', dilate_inputs='-1'),
name='erowm')
regressors.connect([(fillwm, erowm, [('out_file', 'in_file')])])
# generate local wm
localwm = Node(Localstat(neighborhood=('SPHERE', 25),
stat='mean',
nonmask=True,
outputtype='NIFTI_GZ'),
name='localwm')
regressors.connect([(erowm, localwm, [('out_file', 'mask_file')])])
preproc.connect([(coreg, regressors, [('warp.output_image',
'localwm.in_file')])])
# bandpass filter the local wm regressor
localwm_bpf = Node(Fourier(highpass=0.01,
lowpass=0.08,
args='-retrend',
outputtype='NIFTI_GZ'),
name='loacwm_bpf')
regressors.connect([(localwm, localwm_bpf, [('out_file', 'in_file')])])
# detrend the local wm regressor
localwm_bpf_dt = Node(Detrend(args='-polort 2', outputtype='NIFTI_GZ'),
name='localwm_bpf_dt')
regressors.connect([(localwm_bpf, localwm_bpf_dt, [('out_file', 'in_file')
])])
'''
Clean up your functional image with the regressors you have created above
'''
# create a mask for blurring filtering, and detrending
clean = Workflow(name='clean')
mask = Node(BET(mask=True, functional=True), name='mask')
mean_mask = Node(MCFLIRT(mean_vol=True, output_type='NIFTI_GZ'),
name="mean_mask")
dilf = Node(DilateImage(operation='max', output_type='NIFTI_GZ'),
name='dilf')
clean.connect([(mask, dilf, [('mask_file', 'in_file')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'mask.in_file')])])
fill = Node(MaskTool(in_file='default_images/MNI152_T1_2mm_brain.nii.gz',
fill_holes=True,
outputtype='NIFTI_GZ'),
name='fill')
axb = Node(Calc(expr='a*b', outputtype='NIFTI_GZ'), name='axb')
clean.connect([(dilf, axb, [('out_file', 'in_file_a')])])
clean.connect([(fill, axb, [('out_file', 'in_file_b')])])
bxc = Node(Calc(expr='ispositive(a)*b', outputtype='NIFTI_GZ'), name='bxc')
clean.connect([(mean_mask, bxc, [('mean_img', 'in_file_a')])])
clean.connect([(axb, bxc, [('out_file', 'in_file_b')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'mean_mask.in_file')
])])
#### BLUR, FOURIER BPF, and DETREND
blurinmask = Node(BlurInMask(fwhm=6, outputtype='NIFTI_GZ'),
name='blurinmask')
clean.connect([(bxc, blurinmask, [('out_file', 'mask')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'blurinmask.in_file')
])])
fourier = Node(Fourier(highpass=0.01,
lowpass=0.08,
retrend=True,
outputtype='NIFTI_GZ'),
name='fourier')
clean.connect([(blurinmask, fourier, [('out_file', 'in_file')])])
tstat = Node(TStat(args='-mean', outputtype='NIFTI_GZ'), name='tstat')
clean.connect([(fourier, tstat, [('out_file', 'in_file')])])
detrend = Node(Detrend(args='-polort 2', outputtype='NIFTI_GZ'),
name='detrend')
clean.connect([(fourier, detrend, [('out_file', 'in_file')])])
remean = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean')
clean.connect([(detrend, remean, [('out_file', 'in_file_a')])])
clean.connect([(tstat, remean, [('out_file', 'in_file_b')])])
concat = Node(ConcatModel(), name='concat')
# Removes nuisance regressors via regression function
clean_rs = Node(Bandpass(highpass=0, lowpass=99999, outputtype='NIFTI_GZ'),
name='clean_rs')
clean.connect([(concat, clean_rs, [('out_file', 'orthogonalize_file')])])
remean1 = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean1')
clean.connect([(clean_rs, remean1, [('out_file', 'in_file_a')])])
clean.connect([(tstat, remean1, [('out_file', 'in_file_b')])])
preproc.connect([(regressors, clean, [('bpf_dt_csf.out_file',
'concat.in_file_a')])])
preproc.connect([(fproc, clean, [('moco_bpfdt.out_file',
'concat.in_file_b')])])
preproc.connect([(regressors, clean, [('localwm_bpf_dt.out_file',
'clean_rs.orthogonalize_dset')])])
clean.connect([(remean, clean_rs, [('out_file', 'in_file')])])
'''
Write graphical output detailing the workflows and nodes
'''
fproc.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./fproc.dot')
fproc.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./fproc_color.dot')
coreg.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./coreg.dot')
coreg.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./coreg_color.dot')
scrub.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./scrub.dot')
scrub.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./scrub_color.dot')
regressors.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./reg.dot')
regressors.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./reg_color.dot')
preproc.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./preproc.dot')
preproc.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./preproc_color.dot')
return preproc
def COMPOSER(verbose=False, is_bruker=False):
inputnode = Node(IdentityInterface(fields=['in_file', 'ref_file']),
name='input')
outputnode = Node(IdentityInterface(fields=['out_file']), name='output')
wf = Workflow(name='COMPOSER')
in_mag = Node(Complex(magnitude_out_file='in_mag.nii.gz', verbose=verbose),
name='in_magnitude')
ref_mag = Node(Complex(magnitude_out_file='ref_mag.nii.gz',
verbose=verbose),
name='ref_magnitude')
if is_bruker:
wf.connect([(inputnode, in_mag, [('in_file', 'realimag')])])
wf.connect([(inputnode, ref_mag, [('ref_file', 'realimag')])])
else:
wf.connect([(inputnode, in_mag, [('in_file', 'complex')])])
wf.connect([(inputnode, ref_mag, [('ref_file', 'complex')])])
in_mean = Node(maths.MeanImage(), name='in_mean')
ref_mean = Node(maths.MeanImage(), name='ref_mean')
wf.connect([(in_mag, in_mean, [('magnitude_out_file', 'in_file')]),
(ref_mag, ref_mean, [('magnitude_out_file', 'in_file')])])
register = Node(Registration(dimension=3,
initial_moving_transform_com=1,
transforms=['Rigid'],
metric=['Mattes'],
metric_weight=[1],
transform_parameters=[(0.1, )],
number_of_iterations=[[1000, 500, 250]],
collapse_output_transforms=False,
initialize_transforms_per_stage=False,
radius_or_number_of_bins=[32],
sampling_strategy=['Regular', None],
sampling_percentage=[0.25, None],
convergence_threshold=[1.e-6],
smoothing_sigmas=[[4, 2, 1]],
shrink_factors=[[8, 4, 2]],
sigma_units=['vox'],
output_warped_image=True,
verbose=True),
name='register')
wf.connect([(in_mean, register, [('out_file', 'moving_image')]),
(ref_mean, register, [('out_file', 'fixed_image')])])
if is_bruker:
resample = Node(ApplyTransforms(dimension=3, input_image_type=3),
name='resample_reference')
in_x = Node(Complex(complex_out_file='in_x.nii.gz', verbose=verbose),
name='in_x')
ref_x = Node(Complex(complex_out_file='ref_x.nii.gz', verbose=verbose),
name='ref_x')
cc = Node(CoilCombine(), name='cc')
wf.connect([(inputnode, resample, [('ref_file', 'input_image')]),
(in_mean, resample, [('out_file', 'reference_image')]),
(register, resample, [('reverse_transforms', 'transforms')
]),
(inputnode, in_x, [('in_file', 'realimag')]),
(resample, ref_x, [('output_image', 'realimag')]),
(in_x, cc, [('complex_out_file', 'in_file')]),
(ref_x, cc, [('complex_out_file', 'composer_file')]),
(cc, outputnode, [('out_file', 'out_file')])])
else:
raise ('Not Yet Supported')
return wf
def __init__(self, parent, dir_dic, bids):
super().__init__(parent, dir_dic, bids)
# Create interfaces ============================================================================================
# BET
MNI_BET = Node(BET(), name="MNI_BET")
MNI_BET.btn_string = 'MNI Template Brain Extraction'
self.interfaces.append(MNI_BET)
# Registration
postopCT_T1_Reg = Node(Registration(), name="postopCT_T1_Reg")
postopCT_T1_Reg.btn_string = 'post-op CT to T1w Registration'
self.interfaces.append(postopCT_T1_Reg)
preopCT_T1_Reg = Node(Registration(), name="preopCT_T1_Reg")
preopCT_T1_Reg.btn_string = 'pre-op CT to T1w Registration'
self.interfaces.append(preopCT_T1_Reg)
T1_MNI_Reg = Node(Registration(), name="T1_MNI_Reg")
T1_MNI_Reg.btn_string = 'T1w to MNI template Registration'
self.interfaces.append(T1_MNI_Reg)
# Transformations
postopCT_T1_Tran = Node(ApplyTransforms(), name="postopCT_T1_Tran")
postopCT_T1_Tran.btn_string = 'post-op CT to T1w Transformation'
self.interfaces.append(postopCT_T1_Tran)
preopCT_T1_Tran = Node(ApplyTransforms(), name="preopCT_T1_Tran")
preopCT_T1_Tran.btn_string = 'pre-op CT to T1w Transformation'
self.interfaces.append(preopCT_T1_Tran)
T1_MNI_Tran = Node(ApplyTransforms(), name="T1_MNI_Tran")
T1_MNI_Tran.btn_string = 'T1w to MNI template Transformation'
self.interfaces.append(T1_MNI_Tran)
# Data output (i.e. sink) ======================================================================================
self.sink = Node(DataSink(), name="sink")
self.sink.btn_string = 'data sink'
self.sink.inputs.base_directory = self.dir_dic['data_dir']
self.jsink = Node(JSONFileSink(), name="jsink")
self.jsink.btn_string = 'json sink'
self.jsink.inputs.base_directory = self.dir_dic['data_dir']
# Initialize workflow ==========================================================================================
self.wf = Workflow(name='co_registration')
# Brain extracted MNI template to antsRegistration
# MI[mni_t1_brain.nii.gz,t1_nonGdE_brain_N4bfc_masked.nii.gz,1,32,Regular,0.25]
# MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
self.wf.connect([(self.return_interface("MNI_BET"),
self.return_interface("T1_MNI_Reg"),
[("out_file", "fixed_image")])])
self.wf.connect([(self.return_interface("MNI_BET"),
self.return_interface("T1_MNI_Tran"),
[("out_file", "reference_image")])])
# T1 -> MNI Reg to Tran
self.wf.connect([(self.return_interface("T1_MNI_Reg"),
self.return_interface("T1_MNI_Tran"),
[("composite_transform", "transforms")])])
# postop CT -> T1 Reg to Tran
self.wf.connect([(self.return_interface("postopCT_T1_Reg"),
self.return_interface("postopCT_T1_Tran"),
[("composite_transform", "transforms")])])
# preop CT -> T1 Reg to Tran
self.wf.connect([(self.return_interface("preopCT_T1_Reg"),
self.return_interface("preopCT_T1_Tran"),
[("composite_transform", "transforms")])])
# BaseInterface generates a dict mapping button strings to the workflow nodes
self.wf.base_dir = self.dir_dic['temp_dir']
graph_file = self.wf.write_graph("co_registration", graph2use='flat')
self.graph_file = graph_file.replace("co_registration.png",
"co_registration_detailed.png")
self.init_settings()
self.init_ui()
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
path = '/Users/m131199/Documents/segData/Brats2014/'
fileName = 'T2.nii'
for i in range(len(folderNames)):
folder = path+folderNames[i] + '/'
input_fixed = folder + fileName
os.chdir(folder)
folderName = 'atlasTransformations_'+fileName[:len(fileName)-4]
os.mkdir(folderName)
newPath = folder + folderName + '/'
os.chdir(newPath)
# atlasPath = '/Users/m131199/Documents/LGG_GUI/LGG/'
atlasPath = homedir
input_moving = atlasPath + 'atlas_stripped.nii'
reg = Registration()
# ants-registration parameters:
reg.inputs.fixed_image = input_fixed # fixed image
reg.inputs.moving_image = input_moving # moving image
reg.inputs.output_transform_prefix = newPath # file path
reg.inputs.transforms = ['Affine','SyN'] # list of transformations
reg.inputs.transform_parameters = [(.3,),(0.1,3.0,0.0)]
reg.inputs.number_of_iterations = [[40, 20, 10],[40, 20, 10]]
# reg.inputs.number_of_iterations = [[1, 1, 1],[1, 1, 1]]
reg.inputs.dimension = 3
reg.inputs.initial_moving_transform_com = True
#reg.inputs.invert_initial_moving_transform = True
reg.inputs.output_warped_image = True
reg.inputs.output_inverse_warped_image = True
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
AT_to_T1_Fit.inputs.numberOfMatchPoints = 20
AT_to_T1_Fit.inputs.numberOfHistogramBins = 50
AT_to_T1_Fit.inputs.minimumStepLength = [0.001, 0.0001, 0.0001]
AT_to_T1_Fit.inputs.outputVolume = 'AT_to_T1.nii.gz'
AT_to_T1_Fit.inputs.outputVolumePixelType = 'int'
### AT_to_T1_Fit.inputs.interpolationMode='BSpline'
AT_to_T1_Fit.inputs.initializeTransformMode = 'useMomentsAlign' # 'useGeometryAlign'
### AT_to_T1_Fit.inputs.maskProcessingMode="ROIAUTO" ## Images are choppped already, so ROIAUTO should work
### AT_to_T1_Fit.inputs.ROIAutoClosingSize=2 ## Mini pig brains are much smalle than human brains
### AT_to_T1_Fit.inputs.ROIAutoDilateSize=.5 ## Auto dilate a very small amount
minipigWF.connect(chopT1, 'outFN', AT_to_T1_Fit, 'fixedVolume')
minipigWF.connect(fixAtlas, 'outFN', AT_to_T1_Fit, 'movingVolume')
######===========================
BeginANTS = pe.Node(interface=Registration(), name="antsA2S")
##many_cpu_sge_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,8,8,24), 'overwrite': True}
##ComputeAtlasToSubjectTransform.plugin_args = many_cpu_sge_options_dictionary
BeginANTS.inputs.dimension = 3
""" This is the recommended set of parameters from the ANTS developers """
BeginANTS.inputs.output_transform_prefix = 'A2S_output_tfm'
BeginANTS.inputs.transforms = ["Affine", "SyN", "SyN", "SyN"]
BeginANTS.inputs.transform_parameters = [[0.1], [0.1, 3.0, 0.0],
[0.1, 3.0, 0.0], [0.1, 3.0, 0.0]]
BeginANTS.inputs.metric = ['MI', 'CC', 'CC', 'CC']
BeginANTS.inputs.sampling_strategy = ['Regular', None, None, None]
BeginANTS.inputs.sampling_percentage = [0.27, 1.0, 1.0, 1.0]
BeginANTS.inputs.metric_weight = [1.0, 1.0, 1.0, 1.0]
BeginANTS.inputs.radius_or_number_of_bins = [32, 3, 3, 3]
BeginANTS.inputs.number_of_iterations = [[1000, 1000, 1000, 1000], [1000, 250],
print("Downloaded file: {0}".format(localFilename))
else:
print("File previously downloaded {0}".format(localFilename))
input_images = [
os.path.join(mydatadir, '01_T1_half.nii.gz'),
os.path.join(mydatadir, '02_T1_half.nii.gz'),
]
"""
3. Define the parameters of the registration. Settings are
found in the file ``smri_ants_registration_settings.json``
distributed with the ``example_data`` of `nipype`.
"""
reg = Registration(
from_file=example_data('smri_ants_registration_settings.json'))
reg.inputs.fixed_image = input_images[0]
reg.inputs.moving_image = input_images[1]
"""
Alternatively to the use of the ``from_file`` feature to load ANTs settings,
the user can manually set all those inputs instead::
reg.inputs.output_transform_prefix = 'thisTransform'
reg.inputs.output_warped_image = 'INTERNAL_WARPED.nii.gz'
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Translation', 'Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = ([[10000, 111110, 11110]] * 3 +
[[100, 50, 30]])
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
def preprocess(data_dir, subject, atlas_dir, output_dir):
with tempfile.TemporaryDirectory() as temp_dir:
if not os.path.exists(os.path.join(output_dir, subject, 'ANTS_FLAIR_r.nii.gz')):
if os.path.exists(os.path.join(data_dir, subject, 'FLAIR.nii.gz')):
# reorient to MNI standard direction
reorient = fsl.utils.Reorient2Std()
reorient.inputs.in_file = os.path.join(data_dir, subject, 'FLAIR.nii.gz')
reorient.inputs.out_file = os.path.join(temp_dir, 'FLAIR_reorient.nii.gz')
reorient.run()
# robust fov to remove neck and lower head automatically
rf = fsl.utils.RobustFOV()
rf.inputs.in_file = os.path.join(temp_dir, 'FLAIR_reorient.nii.gz')
rf.inputs.out_roi = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
rf.run()
# skull stripping first run
print('BET pre-stripping...')
btr1 = fsl.BET()
btr1.inputs.in_file = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
btr1.inputs.robust = True
btr1.inputs.frac = 0.2
btr1.inputs.out_file = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
btr1.run()
# N4 bias field correction
print('N4 Bias Field Correction running...')
input_image = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
output_image = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
subprocess.call('N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 '
'--input-image %s --convergence [ 50x50x30x20 ] --output %s --shrink-factor 3'
% (input_image, output_image), shell=True)
# registration of FLAIR to MNI152 FLAIR template
print('ANTs registration...')
reg = Registration()
reg.inputs.fixed_image = atlas_dir + '/flair_test.nii.gz'
reg.inputs.moving_image = os.path.join(temp_dir, 'FLAIR_RF.nii.gz')
reg.inputs.output_transform_prefix = os.path.join(output_dir, subject, 'FLAIR_r_transform.mat')
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[1000, 500, 250, 100], [1000, 500, 250, 100], [100, 70, 50, 20]]
reg.inputs.dimension = 3
reg.inputs.initial_moving_transform_com = 0
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.initialize_transforms_per_stage = False
reg.inputs.metric = ['Mattes', 'Mattes', ['Mattes', 'CC']]
reg.inputs.metric_weight = [1, 1, [.5, .5]] # Default (value ignored currently by ANTs)
reg.inputs.radius_or_number_of_bins = [32, 32, [32, 4]]
reg.inputs.sampling_strategy = ['Random', 'Random', None]
reg.inputs.sampling_percentage = [0.25, 0.25, [0.05, 0.10]]
reg.inputs.convergence_threshold = [1e-6, 1.e-6, 1.e-6]
reg.inputs.convergence_window_size = [10] * 3
reg.inputs.smoothing_sigmas = [[3, 2, 1, 0], [3, 2, 1, 0], [3, 2, 1, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[8, 4, 2, 1], [8, 4, 2, 1], [8, 4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True, True, True]
reg.inputs.use_histogram_matching = [True, True, True] # This is the default
reg.inputs.output_warped_image = os.path.join(output_dir, subject, 'ANTS_FLAIR_r.nii.gz')
reg.inputs.verbose = True
reg.run()
# second pass of BET skull stripping
print('BET skull stripping...')
btr2 = fsl.BET()
btr2.inputs.in_file = os.path.join(output_dir, subject, 'ANTS_FLAIR_r.nii.gz')
btr2.inputs.robust = True
btr2.inputs.frac = 0.1
btr2.inputs.mask = True
btr2.inputs.out_file = os.path.join(output_dir, subject, 'ANTS_FLAIR_r.nii.gz')
btr2.run()
# copy mask file to output folder
shutil.copy2(os.path.join(output_dir, subject, 'ANTS_FLAIR_r_mask.nii.gz'),
os.path.join(temp_dir, 'ANTS_FLAIR_r_mask.nii.gz'))
# z score normalization
FLAIR_path = os.path.join(output_dir, subject, 'ANTS_FLAIR_r.nii.gz')
FLAIR_final = nib.load(FLAIR_path)
FLAIR_mask_path = os.path.join(temp_dir, 'ANTS_FLAIR_r_mask.nii.gz')
mask = nib.load(FLAIR_mask_path)
FLAIR_norm = zscore_normalize(FLAIR_final, mask)
nib.save(FLAIR_norm, FLAIR_path)
print('.........................')
print('patient %s registration done' % subject)
else:
pass
else:
pass
localFile.write(remotefile.read())
localFile.close()
print("Downloaded file: {0}".format(localFilename))
else:
print("File previously downloaded {0}".format(localFilename))
input_images=[
os.path.join(mydatadir,'01_T1_half.nii.gz'),
os.path.join(mydatadir,'02_T1_half.nii.gz'),
]
"""
3. Define the parameters of the registration
"""
reg = Registration()
reg.inputs.fixed_image = input_images[0]
reg.inputs.moving_image = input_images[1]
reg.inputs.output_transform_prefix = 'thisTransform'
reg.inputs.output_warped_image = 'INTERNAL_WARPED.nii.gz'
reg.inputs.transforms = ['Translation', 'Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.3, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 0, 0], [10000, 0, 0], [10000, 0, 0], [10, 0, 0]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.metric = ['Mattes']*4
reg.inputs.metric_weight = [1]*4 # Default (value ignored currently by ANTs)
reg.inputs.radius_or_number_of_bins = [32]*4
reg.inputs.sampling_strategy = ['Regular']*3 + [None]
def main(args=None):
args = arg_parser().parse_args(args)
FLAIR = args.FLAIR
MPRAGE = args.T1
prefix=args.prefix + '.'
if args.mask is None:
args.temp_mask = os.path.abspath (args.temp_mask)
args.brain_template = os.path.abspath(args.brain_template)
args.temp_prob = os.path.abspath(args.temp_prob)
if not os.path.isfile(args.temp_mask):
raise Exception("template mask not foud")
if not os.path.isfile(args.brain_template):
raise Exception("brain template mask not foud")
if not os.path.isfile(args.temp_prob):
raise Exception("template probability mask not foud")
elif not os.path.isfile(args.mask):
raise Exception("T1 mask file not foud")
if not os.path.isfile(MPRAGE):
raise Exception("Input T1 file not found")
if not os.path.isfile(FLAIR):
raise Exception("Input FLAIR file not found")
if args.outfolder is not None:
abs_out = os.path.abspath(args.outfolder)
#print(abs_out)
if not os.path.exists(abs_out):
#if selecting a new folder copy the files (not sure how to specify different folder under nipype when it runs sh scripts from ants)
os.mkdir(abs_out)
copyfile(os.path.abspath(MPRAGE),os.path.join(abs_out,os.path.basename(MPRAGE)))
copyfile(os.path.abspath(FLAIR),os.path.join(abs_out,os.path.basename(FLAIR)))
if args.mask is not None:
if os.path.isfile(args.mask):
copyfile(os.path.abspath(args.mask),os.path.join(abs_out, prefix + 'MPRAGE.mask.nii.gz'))
os.chdir(args.outfolder)
elif args.mask is not None:
copyfile(os.path.abspath(args.mask),os.path.join(os.path.abspath(args.mask), prefix + 'MPRAGE.mask.nii.gz'))
if args.mask is None:
# T1 brain extraction
brainextraction = BrainExtraction()
brainextraction.inputs.dimension = 3
brainextraction.inputs.anatomical_image = MPRAGE
brainextraction.inputs.brain_template = args.brain_template
brainextraction.inputs.brain_probability_mask = args.temp_prob
brainextraction.inputs.extraction_registration_mask= args.temp_mask
brainextraction.inputs.debug=True
print("brain extraction")
print(' ')
print(brainextraction.cmdline)
print('-'*30)
brainextraction.run()
os.rename('highres001_BrainExtractionMask.nii.gz',prefix +'MPRAGE.mask.nii.gz')
os.rename('highres001_BrainExtractionBrain.nii.gz',prefix +'MPRAGE.brain.nii.gz')
os.remove('highres001_BrainExtractionPrior0GenericAffine.mat')
os.rmdir('highres001_')
#two step registration with ants (step1)
reg = Registration()
reg.inputs.fixed_image = FLAIR
reg.inputs.moving_image = MPRAGE
reg.inputs.output_transform_prefix = "output_"
reg.inputs.output_warped_image = prefix + 'output_warped_image.nii.gz'
reg.inputs.dimension = 3
reg.inputs.transforms = ['Rigid']
reg.inputs.transform_parameters = [[0.1]]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.metric = ['MI']
reg.inputs.sampling_percentage = [0.1]
reg.inputs.sampling_strategy = ['Regular']
reg.inputs.shrink_factors = [[4,3,2,1]]
reg.inputs.smoothing_sigmas = [[3,2,1,0]]
reg.inputs.sigma_units = ['vox']
reg.inputs.use_histogram_matching = [False]
reg.inputs.number_of_iterations = [[1000,500,250,100]]
reg.inputs.winsorize_lower_quantile = 0.025
reg.inputs.winsorize_upper_quantile = 0.975
print("first pass registration")
print(' ')
print(reg.cmdline)
print('-'*30)
reg.run()
os.rename('output_0GenericAffine.mat',prefix + 'MPRAGE_to_FLAIR.firstpass.mat')
#apply tranform MPRAGE mask to FLAIR
at = ApplyTransforms()
at.inputs.dimension = 3
at.inputs.input_image = prefix + 'MPRAGE.mask.nii.gz'
at.inputs.reference_image = FLAIR
at.inputs.output_image = prefix + 'FLAIR.mask.nii.gz'
at.inputs.interpolation = 'MultiLabel'
at.inputs.default_value = 0
at.inputs.transforms = [ prefix + 'MPRAGE_to_FLAIR.firstpass.mat']
at.inputs.invert_transform_flags = [False]
print("apply stranform to T1 maks")
print(' ')
print(at.cmdline)
print('-'*30)
at.run()
# bias correct FLAIR and MPRAGE
n4m = N4BiasFieldCorrection()
n4m.inputs.dimension = 3
n4m.inputs.input_image = MPRAGE
n4m.inputs.mask_image = prefix + 'MPRAGE.mask.nii.gz'
n4m.inputs.bspline_fitting_distance = 300
n4m.inputs.shrink_factor = 3
n4m.inputs.n_iterations = [50,50,30,20]
n4m.inputs.output_image = prefix + 'MPRAGE.N4.nii.gz'
print("bias correcting T1")
print(' ')
print(n4m.cmdline)
print('-'*30)
n4m.run()
n4f = copy.deepcopy(n4m)
n4f.inputs.input_image = FLAIR
n4f.inputs.mask_image = prefix + 'FLAIR.mask.nii.gz'
n4f.inputs.output_image = prefix + 'FLAIR.N4.nii.gz'
print("bias correcting FLAIR")
print(' ')
print(n4f.cmdline)
print('-'*30)
n4f.run()
# mask bias corrected FLAIR and MPRAGE
calc = afni.Calc()
calc.inputs.in_file_a = prefix + 'FLAIR.N4.nii.gz'
calc.inputs.in_file_b = prefix + 'FLAIR.mask.nii.gz'
calc.inputs.expr='a*b'
calc.inputs.out_file = prefix + 'FLAIR.N4.masked.nii.gz'
calc.inputs.outputtype = 'NIFTI'
calc.inputs.overwrite = True
calc.run()
calc1= copy.deepcopy(calc)
calc1.inputs.in_file_a = prefix + 'MPRAGE.N4.nii.gz'
calc1.inputs.in_file_b = prefix + 'MPRAGE.mask.nii.gz'
calc1.inputs.out_file = prefix + 'MPRAGE.N4.masked.nii.gz'
calc1.inputs.overwrite = True
calc1.run()
#register bias corrected
reg1 = copy.deepcopy(reg)
reg1.inputs.output_transform_prefix = "output_"
reg1.inputs.output_warped_image = prefix + 'output_warped_image.nii.gz'
reg1.inputs.initial_moving_transform = prefix +'MPRAGE_to_FLAIR.firstpass.mat'
print("second pass registration")
print(' ')
print(reg1.cmdline)
print('-'*30)
reg1.run()
os.rename('output_0GenericAffine.mat',prefix +'MPRAGE_to_FLAIR.secondpass.mat')
#generate final mask in FLAIR space
atf = ApplyTransforms()
atf.inputs.dimension = 3
atf.inputs.input_image = prefix + 'MPRAGE.N4.nii.gz'
atf.inputs.reference_image = FLAIR
atf.inputs.output_image = prefix + 'MPRAGE.N4.toFLAIR.nii.gz'
atf.inputs.interpolation = 'BSpline'
atf.inputs.interpolation_parameters = (3,)
atf.inputs.default_value = 0
atf.inputs.transforms = [prefix + 'MPRAGE_to_FLAIR.secondpass.mat']
atf.inputs.invert_transform_flags = [False]
print("final apply transform")
print(' ')
print(atf.cmdline)
print('-'*30)
atf.run()
#cleanup
os.remove(prefix + 'output_warped_image.nii.gz')
if args.outfolder is not None:
os.remove(os.path.join(abs_out,os.path.basename(MPRAGE)))
os.remove(os.path.join(abs_out,os.path.basename(FLAIR)))
if args.mask is None:
os.remove(prefix + 'MPRAGE.brain.nii.gz')
if not args.storetemp:
os.remove(prefix + 'MPRAGE.mask.nii.gz')
os.remove(prefix + 'MPRAGE_to_FLAIR.firstpass.mat')
os.remove(prefix + 'FLAIR.N4.masked.nii.gz')
os.remove(prefix + 'MPRAGE.N4.masked.nii.gz')
os.remove(prefix + 'MPRAGE.N4.nii.gz')
return
def embedded_antsreg_2d(source_image,
target_image,
run_rigid=False,
rigid_iterations=1000,
run_affine=False,
affine_iterations=1000,
run_syn=True,
coarse_iterations=40,
medium_iterations=50,
fine_iterations=40,
cost_function='MutualInformation',
interpolation='NearestNeighbor',
convergence=1e-6,
ignore_affine=False,
ignore_header=False,
save_data=False,
overwrite=False,
output_dir=None,
file_name=None):
""" Embedded ANTS Registration 2D
Runs the rigid and/or Symmetric Normalization (SyN) algorithm of ANTs and
formats the output deformations into voxel coordinate mappings as used in
CBSTools registration and transformation routines.
Parameters
----------
source_image: niimg
Image to register
target_image: niimg
Reference image to match
run_rigid: bool
Whether or not to run a rigid registration first (default is False)
rigid_iterations: float
Number of iterations in the rigid step (default is 1000)
run_affine: bool
Whether or not to run a affine registration first (default is False)
affine_iterations: float
Number of iterations in the affine step (default is 1000)
run_syn: bool
Whether or not to run a SyN registration (default is True)
coarse_iterations: float
Number of iterations at the coarse level (default is 40)
medium_iterations: float
Number of iterations at the medium level (default is 50)
fine_iterations: float
Number of iterations at the fine level (default is 40)
cost_function: {'CrossCorrelation', 'MutualInformation'}
Cost function for the registration (default is 'MutualInformation')
interpolation: {'NearestNeighbor', 'Linear'}
Interpolation for the registration result (default is 'NearestNeighbor')
convergence: flaot
Threshold for convergence, can make the algorithm very slow
(default is convergence)
ignore_affine: bool
Ignore the affine matrix information extracted from the image header
(default is False)
ignore_header: bool
Ignore the orientation information and affine matrix information
extracted from the image header (default is False)
save_data: bool
Save output data to file (default is False)
overwrite: bool
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* transformed_source (niimg): Deformed source image (_ants-def)
* mapping (niimg): Coordinate mapping from source to target (_ants-map)
* inverse (niimg): Inverse coordinate mapping from target to source
(_ants-invmap)
Notes
----------
Port of the CBSTools Java module by Pierre-Louis Bazin. The main algorithm
is part of the ANTs software by Brian Avants and colleagues [1]_. The
interfacing with ANTs is performed through Nipype [2]_. Parameters have been
set to values commonly found in neuroimaging scripts online, but not
necessarily optimal.
References
----------
.. [1] Avants et al (2008), Symmetric diffeomorphic
image registration with cross-correlation: evaluating automated labeling
of elderly and neurodegenerative brain, Med Image Anal. 12(1):26-41
.. [2] Gorgolewski et al (2011) Nipype: a flexible, lightweight and
extensible neuroimaging data processing framework in python.
Front Neuroinform 5. doi:10.3389/fninf.2011.00013
"""
print('\nEmbedded ANTs Registration')
# for external tools: nipype
try:
from nipype.interfaces.ants import Registration
from nipype.interfaces.ants import ApplyTransforms
except ImportError:
print(
'Error: Nipype and/or ANTS could not be imported, they are required'
+ ' in order to run this module. \n (aborting)')
return None
# make sure that saving related parameters are correct
output_dir = _output_dir_4saving(
output_dir, source_image) # needed for intermediate results
if save_data:
transformed_source_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='ants-def'))
mapping_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='ants-map'))
inverse_mapping_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='ants-invmap'))
if overwrite is False \
and os.path.isfile(transformed_source_file) \
and os.path.isfile(mapping_file) \
and os.path.isfile(inverse_mapping_file) :
print("skip computation (use existing results)")
output = {
'transformed_source': load_volume(transformed_source_file),
'mapping': load_volume(mapping_file),
'inverse': load_volume(inverse_mapping_file)
}
return output
# load and get dimensions and resolution from input images
source = load_volume(source_image)
src_affine = source.affine
src_header = source.header
nsx = source.header.get_data_shape()[X]
nsy = source.header.get_data_shape()[Y]
nsz = 1
rsx = source.header.get_zooms()[X]
rsy = source.header.get_zooms()[Y]
rsz = 1
target = load_volume(target_image)
trg_affine = target.affine
trg_header = target.header
ntx = target.header.get_data_shape()[X]
nty = target.header.get_data_shape()[Y]
ntz = 1
rtx = target.header.get_zooms()[X]
rty = target.header.get_zooms()[Y]
rtz = 1
# in case the affine transformations are not to be trusted: make them equal
if ignore_affine or ignore_header:
mx = np.argmax(np.abs(src_affine[0][0:3]))
my = np.argmax(np.abs(src_affine[1][0:3]))
mz = np.argmax(np.abs(src_affine[2][0:3]))
new_affine = np.zeros((4, 4))
if ignore_header:
new_affine[0][0] = rsx
new_affine[1][1] = rsy
new_affine[2][2] = rsz
new_affine[0][3] = -rsx * nsx / 2.0
new_affine[1][3] = -rsy * nsy / 2.0
new_affine[2][3] = -rsz * nsz / 2.0
else:
new_affine[0][mx] = rsx * np.sign(src_affine[0][mx])
new_affine[1][my] = rsy * np.sign(src_affine[1][my])
new_affine[2][mz] = rsz * np.sign(src_affine[2][mz])
if (np.sign(src_affine[0][mx]) < 0):
new_affine[0][3] = rsx * nsx / 2.0
else:
new_affine[0][3] = -rsx * nsx / 2.0
if (np.sign(src_affine[1][my]) < 0):
new_affine[1][3] = rsy * nsy / 2.0
else:
new_affine[1][3] = -rsy * nsy / 2.0
if (np.sign(src_affine[2][mz]) < 0):
new_affine[2][3] = rsz * nsz / 2.0
else:
new_affine[2][3] = -rsz * nsz / 2.0
#if (np.sign(src_affine[0][mx])<0): new_affine[mx][3] = rsx*nsx
#if (np.sign(src_affine[1][my])<0): new_affine[my][3] = rsy*nsy
#if (np.sign(src_affine[2][mz])<0): new_affine[mz][3] = rsz*nsz
#new_affine[0][3] = nsx/2.0
#new_affine[1][3] = nsy/2.0
#new_affine[2][3] = nsz/2.0
new_affine[3][3] = 1.0
src_img = nb.Nifti1Image(source.get_data(), new_affine, source.header)
src_img.update_header()
src_img_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='tmp_srcimg'))
save_volume(src_img_file, src_img)
source = load_volume(src_img_file)
src_affine = source.affine
src_header = source.header
# create generic affine aligned with the orientation for the target
mx = np.argmax(np.abs(trg_affine[0][0:3]))
my = np.argmax(np.abs(trg_affine[1][0:3]))
mz = np.argmax(np.abs(trg_affine[2][0:3]))
new_affine = np.zeros((4, 4))
if ignore_header:
new_affine[0][0] = rtx
new_affine[1][1] = rty
new_affine[2][2] = rtz
new_affine[0][3] = -rtx * ntx / 2.0
new_affine[1][3] = -rty * nty / 2.0
new_affine[2][3] = -rtz * ntz / 2.0
else:
new_affine[0][mx] = rtx * np.sign(trg_affine[0][mx])
new_affine[1][my] = rty * np.sign(trg_affine[1][my])
new_affine[2][mz] = rtz * np.sign(trg_affine[2][mz])
if (np.sign(trg_affine[0][mx]) < 0):
new_affine[0][3] = rtx * ntx / 2.0
else:
new_affine[0][3] = -rtx * ntx / 2.0
if (np.sign(trg_affine[1][my]) < 0):
new_affine[1][3] = rty * nty / 2.0
else:
new_affine[1][3] = -rty * nty / 2.0
if (np.sign(trg_affine[2][mz]) < 0):
new_affine[2][3] = rtz * ntz / 2.0
else:
new_affine[2][3] = -rtz * ntz / 2.0
#if (np.sign(trg_affine[0][mx])<0): new_affine[mx][3] = rtx*ntx
#if (np.sign(trg_affine[1][my])<0): new_affine[my][3] = rty*nty
#if (np.sign(trg_affine[2][mz])<0): new_affine[mz][3] = rtz*ntz
#new_affine[0][3] = ntx/2.0
#new_affine[1][3] = nty/2.0
#new_affine[2][3] = ntz/2.0
new_affine[3][3] = 1.0
trg_img = nb.Nifti1Image(target.get_data(), new_affine, target.header)
trg_img.update_header()
trg_img_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='tmp_trgimg'))
save_volume(trg_img_file, trg_img)
target = load_volume(trg_img_file)
trg_affine = target.affine
trg_header = target.header
# build coordinate mapping matrices and save them to disk
src_coord = np.zeros((nsx, nsy, 2))
trg_coord = np.zeros((ntx, nty, 2))
for x in range(nsx):
for y in range(nsy):
src_coord[x, y, X] = x
src_coord[x, y, Y] = y
src_map = nb.Nifti1Image(src_coord, source.affine, source.header)
src_map_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='tmp_srccoord'))
save_volume(src_map_file, src_map)
for x in range(ntx):
for y in range(nty):
trg_coord[x, y, X] = x
trg_coord[x, y, Y] = y
trg_map = nb.Nifti1Image(trg_coord, target.affine, target.header)
trg_map_file = os.path.join(
output_dir,
_fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='tmp_trgcoord'))
save_volume(trg_map_file, trg_map)
# run the main ANTS software
reg = Registration()
reg.inputs.dimension = 2
# add a prefix to avoid multiple names?
prefix = _fname_4saving(file_name=file_name,
rootfile=source_image,
suffix='tmp_syn')
prefix = os.path.basename(prefix)
prefix = prefix.split(".")[0]
reg.inputs.output_transform_prefix = prefix
reg.inputs.fixed_image = [target.get_filename()]
reg.inputs.moving_image = [source.get_filename()]
print("registering " + source.get_filename() + "\n to " +
target.get_filename())
if run_rigid is True and run_affine is True and run_syn is True:
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [
[rigid_iterations, rigid_iterations, rigid_iterations],
[affine_iterations, affine_iterations, affine_iterations],
[
coarse_iterations, coarse_iterations, medium_iterations,
fine_iterations
]
]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC', 'CC', 'CC']
reg.inputs.metric_weight = [1.0, 1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [5, 5, 5]
else:
reg.inputs.metric = ['MI', 'MI', 'MI']
reg.inputs.metric_weight = [1.0, 1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [32, 32, 32]
reg.inputs.shrink_factors = [[4, 2, 1]] + [[4, 2, 1]] + [[8, 4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]] + [[3, 2, 1]
] + [[2, 1, 0.5, 0]]
reg.inputs.sampling_strategy = ['Random'] + ['Random'] + ['Random']
reg.inputs.sampling_percentage = [0.3] + [0.3] + [0.3]
reg.inputs.convergence_threshold = [convergence] + [convergence
] + [convergence]
reg.inputs.convergence_window_size = [10] + [10] + [5]
reg.inputs.use_histogram_matching = [False] + [False] + [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is True and run_affine is False and run_syn is True:
reg.inputs.transforms = ['Rigid', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [
[rigid_iterations, rigid_iterations, rigid_iterations],
[
coarse_iterations, coarse_iterations, medium_iterations,
fine_iterations
]
]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC', 'CC']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [5, 5]
else:
reg.inputs.metric = ['MI', 'MI']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [32, 32]
reg.inputs.shrink_factors = [[4, 2, 1]] + [[8, 4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]] + [[2, 1, 0.5, 0]]
reg.inputs.sampling_strategy = ['Random'] + ['Random']
reg.inputs.sampling_percentage = [0.3] + [0.3]
reg.inputs.convergence_threshold = [convergence] + [convergence]
reg.inputs.convergence_window_size = [10] + [5]
reg.inputs.use_histogram_matching = [False] + [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is False and run_affine is True and run_syn is True:
reg.inputs.transforms = ['Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [
[affine_iterations, affine_iterations, affine_iterations],
[
coarse_iterations, coarse_iterations, medium_iterations,
fine_iterations
]
]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC', 'CC']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [5, 5]
else:
reg.inputs.metric = ['MI', 'MI']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [64, 64]
reg.inputs.shrink_factors = [[4, 2, 1]] + [[8, 4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]] + [[2, 1, 0.5, 0]]
reg.inputs.sampling_strategy = ['Random'] + ['Random']
reg.inputs.sampling_percentage = [0.3] + [0.3]
reg.inputs.convergence_threshold = [convergence] + [convergence]
reg.inputs.convergence_window_size = [10] + [5]
reg.inputs.use_histogram_matching = [False] + [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
if run_rigid is True and run_affine is True and run_syn is False:
reg.inputs.transforms = ['Rigid', 'Affine']
reg.inputs.transform_parameters = [(0.1, ), (0.1, )]
reg.inputs.number_of_iterations = [[
rigid_iterations, rigid_iterations, rigid_iterations
], [affine_iterations, affine_iterations, affine_iterations]]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC', 'CC']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [5, 5]
else:
reg.inputs.metric = ['MI', 'MI']
reg.inputs.metric_weight = [1.0, 1.0]
reg.inputs.radius_or_number_of_bins = [32, 32]
reg.inputs.shrink_factors = [[4, 2, 1]] + [[4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]] + [[3, 2, 1]]
reg.inputs.sampling_strategy = ['Random'] + ['Random']
reg.inputs.sampling_percentage = [0.3] + [0.3]
reg.inputs.convergence_threshold = [convergence] + [convergence]
reg.inputs.convergence_window_size = [10] + [10]
reg.inputs.use_histogram_matching = [False] + [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is True and run_affine is False and run_syn is False:
reg.inputs.transforms = ['Rigid']
reg.inputs.transform_parameters = [(0.1, )]
reg.inputs.number_of_iterations = [[
rigid_iterations, rigid_iterations, rigid_iterations
]]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [5]
else:
reg.inputs.metric = ['MI']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.shrink_factors = [[4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]]
reg.inputs.sampling_strategy = ['Random']
reg.inputs.sampling_percentage = [0.3]
reg.inputs.convergence_threshold = [convergence]
reg.inputs.convergence_window_size = [10]
reg.inputs.use_histogram_matching = [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is False and run_affine is True and run_syn is False:
reg.inputs.transforms = ['Affine']
reg.inputs.transform_parameters = [(0.1, )]
reg.inputs.number_of_iterations = [[
affine_iterations, affine_iterations, affine_iterations
]]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [5]
else:
reg.inputs.metric = ['MI']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.shrink_factors = [[4, 2, 1]]
reg.inputs.smoothing_sigmas = [[3, 2, 1]]
reg.inputs.sampling_strategy = ['Random']
reg.inputs.sampling_percentage = [0.3]
reg.inputs.convergence_threshold = [convergence]
reg.inputs.convergence_window_size = [10]
reg.inputs.use_histogram_matching = [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is False and run_affine is False and run_syn is True:
reg.inputs.transforms = ['SyN']
reg.inputs.transform_parameters = [(0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[
coarse_iterations, coarse_iterations, medium_iterations,
fine_iterations
]]
if (cost_function == 'CrossCorrelation'):
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [5]
else:
reg.inputs.metric = ['MI']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [32]
reg.inputs.shrink_factors = [[8, 4, 2, 1]]
reg.inputs.smoothing_sigmas = [[2, 1, 0.5, 0]]
reg.inputs.sampling_strategy = ['Random']
reg.inputs.sampling_percentage = [0.3]
reg.inputs.convergence_threshold = [convergence]
reg.inputs.convergence_window_size = [10]
reg.inputs.use_histogram_matching = [False]
reg.inputs.winsorize_lower_quantile = 0.001
reg.inputs.winsorize_upper_quantile = 0.999
elif run_rigid is False and run_affine is False and run_syn is False:
reg.inputs.transforms = ['Rigid']
reg.inputs.transform_parameters = [(0.1, )]
reg.inputs.number_of_iterations = [[0]]
reg.inputs.metric = ['CC']
reg.inputs.metric_weight = [1.0]
reg.inputs.radius_or_number_of_bins = [5]
reg.inputs.shrink_factors = [[1]]
reg.inputs.smoothing_sigmas = [[1]]
print(reg.cmdline)
result = reg.run()
# Transforms the moving image
at = ApplyTransforms()
at.inputs.dimension = 2
at.inputs.input_image = source.get_filename()
at.inputs.reference_image = target.get_filename()
at.inputs.interpolation = interpolation
at.inputs.transforms = result.outputs.forward_transforms
at.inputs.invert_transform_flags = result.outputs.forward_invert_flags
print(at.cmdline)
transformed = at.run()
# Create coordinate mappings
src_at = ApplyTransforms()
src_at.inputs.dimension = 2
src_at.inputs.input_image_type = 3
src_at.inputs.input_image = src_map.get_filename()
src_at.inputs.reference_image = target.get_filename()
src_at.inputs.interpolation = 'Linear'
src_at.inputs.transforms = result.outputs.forward_transforms
src_at.inputs.invert_transform_flags = result.outputs.forward_invert_flags
mapping = src_at.run()
trg_at = ApplyTransforms()
trg_at.inputs.dimension = 2
trg_at.inputs.input_image_type = 3
trg_at.inputs.input_image = trg_map.get_filename()
trg_at.inputs.reference_image = source.get_filename()
trg_at.inputs.interpolation = 'Linear'
trg_at.inputs.transforms = result.outputs.reverse_transforms
trg_at.inputs.invert_transform_flags = result.outputs.reverse_invert_flags
inverse = trg_at.run()
# pad coordinate mapping outside the image? hopefully not needed...
# collect outputs and potentially save
transformed_img = nb.Nifti1Image(
nb.load(transformed.outputs.output_image).get_data(), target.affine,
target.header)
mapping_img = nb.Nifti1Image(
nb.load(mapping.outputs.output_image).get_data(), target.affine,
target.header)
inverse_img = nb.Nifti1Image(
nb.load(inverse.outputs.output_image).get_data(), source.affine,
source.header)
outputs = {
'transformed_source': transformed_img,
'mapping': mapping_img,
'inverse': inverse_img
}
# clean-up intermediate files
os.remove(src_map_file)
os.remove(trg_map_file)
if ignore_affine or ignore_header:
os.remove(src_img_file)
os.remove(trg_img_file)
for name in result.outputs.forward_transforms:
if os.path.exists(name): os.remove(name)
for name in result.outputs.reverse_transforms:
if os.path.exists(name): os.remove(name)
os.remove(transformed.outputs.output_image)
os.remove(mapping.outputs.output_image)
os.remove(inverse.outputs.output_image)
if save_data:
save_volume(transformed_source_file, transformed_img)
save_volume(mapping_file, mapping_img)
save_volume(inverse_mapping_file, inverse_img)
return outputs
"""Test low-iteration 2-stage (Affine, SyN) registration to save computations
Parameters are chosen from John Muschelli's extrantsr::reg_write default parameters
May upgrade with access to computational resources
NOTE: These parameters were used in the preproc workflow
"""
from nipype.interfaces.ants import Registration
reg = Registration()
from nipype.interfaces.fsl import Info
template_path = Info.standard_image('MNI152_T1_1mm_brain.nii.gz')
reg = Registration()
reg.inputs.fixed_image = template_path
reg.inputs.moving_image = '../../data/ds000171/sub-control01/anat/sub-control01_T1w.nii.gz'
# Choose the type of transforms and in what order to implement
reg.inputs.transforms = ['Affine', 'SyN']
reg.inputs.metric = ['Mattes', 'Mattes']
reg.inputs.metric_weight = [1] * 2
reg.inputs.radius_or_number_of_bins = [32] * 2
reg.inputs.sampling_strategy = ['Regular', None]
reg.inputs.sampling_percentage = [0.2, 1]
# Parameters are (GradientStep, updateFieldVarianceInVoxelSpace, totalFieldVarianceInVoxelSpace)
reg.inputs.transform_parameters = [(0.25, ), (0.2, 3.0, 0.0)]
# Choose shinking factors and kernel size per iteration
reg.inputs.number_of_iterations = [[2100, 1200, 1200, 0], [40, 20, 0]]
