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 __init__(self, parent, dir_dic, bids):
super().__init__(parent, dir_dic, bids)
# Create interfaces ============================================================================================
# BET
T1w_BET = Node(BET(), name="T1w_BET")
T1w_BET.btn_string = 'T1w Brain Extraction'
self.interfaces.append(T1w_BET)
T1w_gad_BET = Node(BET(), name="T1w_gad_BET")
T1w_gad_BET.btn_string = 'T1w Gadolinium Enhanced Brain Extraction'
self.interfaces.append(T1w_gad_BET)
T2w_dbs_BET = Node(BET(), name="T2w_dbs_BET")
T2w_dbs_BET.btn_string = 'T2w DBS Acquisition Brain Extraction'
self.interfaces.append(T2w_dbs_BET)
dwi_BET = Node(BET(), name="dwi_BET")
dwi_BET.btn_string = 'dwi Brain Extraction'
self.interfaces.append(dwi_BET)
# BFC
T1w_BFC = Node(N4BiasFieldCorrection(), name="T1w_BFC")
T1w_BFC.btn_string = 'T1w Bias Field Correction'
self.interfaces.append(T1w_BFC)
# Split
dwi_ROI_b0 = Node(ExtractROI(), name="dwi_ROI_b0")
dwi_ROI_b0.btn_string = 'dwi Extract b0'
self.interfaces.append(dwi_ROI_b0)
# Eddy current correction
dwi_Eddy = Node(Eddy(), name="dwi_Eddy")
dwi_Eddy.btn_string = 'dwi Eddy Current Correction'
self.interfaces.append(dwi_Eddy)
# Distortion correction
# as this section is script/comment heavy it was put into a function
self.distortion_correction_workflow()
# 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='pre_processing')
# T1w BET to ants N4BiasFieldCorrection
self.wf.connect([(self.return_interface("T1w_BET"),
self.return_interface("T1w_BFC"),
[("out_file", "input_image")])])
self.wf.connect([(self.return_interface("T1w_BET"),
self.return_interface("T1w_BFC"), [("mask_file",
"mask_image")])])
# Eddy
self.wf.connect([(self.return_interface("dwi_BET"),
self.return_interface("dwi_Eddy"), [("out_file",
"in_file")])])
self.wf.connect([(self.return_interface("dwi_BET"),
self.return_interface("dwi_Eddy"), [("mask_file",
"in_mask")])])
# ROI b0
self.wf.connect([(self.return_interface("dwi_Eddy"),
self.return_interface("dwi_ROI_b0"),
[("out_corrected", "in_file")])])
# Distortion Correction:
# b0_T1_Reg:
# -i: moving image
# -r: T1
# -x: T1 mask
self.wf.connect([(self.return_interface("dwi_ROI_b0"),
self.return_interface("b0_T1w_Reg"),
[("roi_file", "moving_image")])])
self.wf.connect([(self.return_interface("T1w_BFC"),
self.return_interface("b0_T1w_Reg"),
[("output_image", "fixed_image")])])
# test remove as doesn't seem useful (see self.distortion_correction_workflow()) and causes a crash when added
# self.wf.connect([(self.return_interface("T1w_BET"), self.return_interface("b0_T1w_Reg"),
# [("mask_file", "fixed_image_mask")])])
# dwi_T1_Tran:
# -i: Eddy corrected image
# -r: Eddy corrected b0
# -t: transforms
self.wf.connect([(self.return_interface("dwi_Eddy"),
self.return_interface("dwi_T1w_Tran"),
[("out_corrected", "input_image")])])
self.wf.connect([(self.return_interface("dwi_ROI_b0"),
self.return_interface("dwi_T1w_Tran"),
[("roi_file", "reference_image")])])
self.wf.connect([(self.return_interface("b0_T1w_Reg"),
self.return_interface("dwi_T1w_Tran"),
[("composite_transform", "transforms")])])
# BaseInterface generates a dict mapping button strings to the workflow nodes
# self.map_workflow()
graph_file = self.wf.write_graph("pre_processing", graph2use='flat')
self.graph_file = graph_file.replace("pre_processing.png",
"pre_processing_detailed.png")
self.init_settings()
self.init_ui()
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()
