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Removed md flair (#237)
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removed everything from FLAIR MD related code
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@davidmeunier79
davidmeunier79 authored Jan 2, 2024
1 parent 3c10f82 commit 3361888
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Showing 4 changed files with 2 additions and 581 deletions.
15 changes: 0 additions & 15 deletions macapype/nodes/prepare.py
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Expand Up @@ -426,21 +426,6 @@ def padding_cropped_img(cropped_img_file, orig_img_file, indiv_crop):
return padded_img_file


def reg_aladin_dirty(reference, in_file):

import os

cwd = os.path.abspath("")
os.chdir(cwd)
out_file = os.path.abspath("outputResult.nii")
cmd = "reg_aladin -flo {} -ref {} -res {}".format(
in_file, reference, out_file)
os.system(cmd)

assert os.path.exists(out_file)
return out_file


if __name__ == '__main__':

data_path = "/hpc/meca/data/Macaques/Macaque_hiphop/results/ucdavis"
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253 changes: 0 additions & 253 deletions macapype/pipelines/full_pipelines.py
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Expand Up @@ -17,8 +17,6 @@
from macapype.nodes.prepare import padding_cropped_img

from .prepare import (create_short_preparation_pipe,
create_short_preparation_FLAIR_pipe,
create_short_preparation_MD_pipe,
create_short_preparation_T1_pipe,
create_long_multi_preparation_pipe,
create_long_single_preparation_pipe,)
Expand Down Expand Up @@ -1138,257 +1136,6 @@ def create_full_spm_subpipes(
return seg_pipe


###############################################################################
# FLAIR after SPM based segmentation
# -soft SPM_FLAIR or SPM_T1_FLAIR
###############################################################################
def create_transfo_FLAIR_pipe(params_template, params={},
name='transfo_FLAIR_pipe'):
""" Description: apply tranformation to FLAIR, MD and FA if necssary
Processing steps:
- -coreg FA on T1
- apply coreg on MD
- debias using T1xT2BiasFieldCorrection (using mask is betcrop)
- registration to template file with IterREGBET
- SPM segmentation the old way (SPM8, not dartel based)
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`)
- debias (see :class:`T1xT2BiasFieldCorrection \
<macapype.nodes.correct_bias.T1xT2BiasFieldCorrection>`) - also available \
as :ref:`indiv_params <indiv_params>`
- reg (see :class:`IterREGBET <macapype.nodes.register.IterREGBET>`) - \
also available as :ref:`indiv_params <indiv_params>`
- old_segment_pipe (see :class:`create_old_segment_pipe \
<macapype.pipelines.segment.create_old_segment_pipe>`)
- nii_to_mesh_fs_pipe (see :class:`create_nii_to_mesh_fs_pipe \
<macapype.pipelines.surface.create_nii_to_mesh_fs_pipe>`)
Inputs:
inputnode:
SS_T1:
T1 file names
orig_T1:
T2 file names
FLAIR:
flair file name
transfo_file:
Transformation file between native to template
inv_transfo_file:
Transformation file between template and native
threshold_wm:
gm binary tissue in template space
indiv_params (opt):
dict with individuals parameters for some nodes
outputnode:
coreg_FLAIR:
FLAIR coregistered to T1
norm_FLAIR:
FLAIR normalised in template space
arguments:
params_template:
dict of template files containing brain_template and priors \
(list of template based segmented tissues)
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_spm_subpipes")
Outputs:
"""

print("Transfo FLAIR pipe name: ", name)

# Creating pipeline
transfo_pipe = pe.Workflow(name=name)

# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(
fields=['orig_T1', 'FLAIR', 'lin_transfo_file']),
name='inputnode'
)

data_preparation_pipe = create_short_preparation_FLAIR_pipe(
params=parse_key(params, "short_preparation_FLAIR_pipe"))

transfo_pipe.connect(inputnode, 'orig_T1',
data_preparation_pipe, 'inputnode.orig_T1')
transfo_pipe.connect(inputnode, 'FLAIR',
data_preparation_pipe, 'inputnode.FLAIR')

# apply norm to FLAIR
norm_lin_FLAIR = pe.Node(fsl.ApplyXFM(), name="norm_lin_FLAIR")
norm_lin_FLAIR.inputs.reference = params_template["template_brain"]

transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_FLAIR',
norm_lin_FLAIR, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_FLAIR, 'in_matrix_file')

# Creating output node
outputnode = pe.Node(
niu.IdentityInterface(
fields=['coreg_FLAIR', 'norm_FLAIR']),
name='outputnode'
)

transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_FLAIR',
outputnode, 'coreg_FLAIR')

transfo_pipe.connect(norm_lin_FLAIR, 'out_file',
outputnode, 'norm_FLAIR')

return transfo_pipe


# SPM with MD
def create_transfo_MD_pipe(params_template, params={},
name='transfo_MD_pipe'):
""" Description: apply tranformation to FLAIR, MD and FA if necssary
Processing steps:
- -coreg FA on T1
- apply coreg on MD
- debias using T1xT2BiasFieldCorrection (using mask is betcrop)
- registration to template file with IterREGBET
- SPM segmentation the old way (SPM8, not dartel based)
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`)
- debias (see :class:`T1xT2BiasFieldCorrection \
<macapype.nodes.correct_bias.T1xT2BiasFieldCorrection>`) - also available \
as :ref:`indiv_params <indiv_params>`
- reg (see :class:`IterREGBET <macapype.nodes.register.IterREGBET>`) - \
also available as :ref:`indiv_params <indiv_params>`
- old_segment_pipe (see :class:`create_old_segment_pipe \
<macapype.pipelines.segment.create_old_segment_pipe>`)
- nii_to_mesh_fs_pipe (see :class:`create_nii_to_mesh_fs_pipe \
<macapype.pipelines.surface.create_nii_to_mesh_fs_pipe>`)
Inputs:
inputnode:
SS_T1:
T1 file names
orig_T1:
T2 file names
FLAIR:
flair file name
transfo_file:
Transformation file between native to template
inv_transfo_file:
Transformation file between template and native
threshold_wm:
gm binary tissue in template space
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dict of template files containing brain_template and priors \
(list of template based segmented tissues)
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_spm_subpipes")
Outputs:
"""

print("Transfo MD pipe name: ", name)

# Creating pipeline
transfo_pipe = pe.Workflow(name=name)

# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(
fields=['orig_T1', 'SS_T2', 'MD', 'b0mean',
'threshold_wm', 'lin_transfo_file',
'inv_lin_transfo_file']),
name='inputnode'
)

compute_native_wm = pe.Node(fsl.ApplyXFM(), name='compute_native_wm')

transfo_pipe.connect(inputnode, 'threshold_wm',
compute_native_wm, 'in_file')

transfo_pipe.connect(inputnode, 'orig_T1',
compute_native_wm, 'reference')

transfo_pipe.connect(inputnode, 'inv_lin_transfo_file',
compute_native_wm, 'in_matrix_file')

data_preparation_pipe = create_short_preparation_MD_pipe(
params=parse_key(params, "short_preparation_MD_pipe"))

transfo_pipe.connect(inputnode, 'SS_T2',
data_preparation_pipe, 'inputnode.SS_T2')
transfo_pipe.connect(inputnode, 'MD',
data_preparation_pipe, 'inputnode.MD')
transfo_pipe.connect(inputnode, 'b0mean',
data_preparation_pipe, 'inputnode.b0mean')
transfo_pipe.connect(compute_native_wm, 'out_file',
data_preparation_pipe, 'inputnode.native_wm_mask')

# apply norm to coreg_MD
norm_lin_MD = pe.Node(fsl.ApplyXFM(), name="norm_lin_MD")
norm_lin_MD.inputs.reference = params_template["template_brain"]

transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_MD',
norm_lin_MD, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_MD, 'in_matrix_file')

# apply norm to coreg_better_MD
norm_lin_better_MD = pe.Node(fsl.ApplyXFM(), name="norm_lin_better_MD")
norm_lin_better_MD.inputs.reference = params_template["template_brain"]

transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_better_MD',
norm_lin_better_MD, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_better_MD, 'in_matrix_file')

return transfo_pipe


###############################################################################
# ANTS based segmentation (from Kepkee Loh / Julien Sein)
# (-soft ANTS)
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