/
TrackNorm.py
328 lines (261 loc) · 12.9 KB
/
TrackNorm.py
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import nipype.interfaces.io as nio # Data i/o
import nipype.interfaces.utility as util # utility
from nipype.interfaces.utility import Function
import os
import os.path as op
import nipype.algorithms.misc as misc
import nipype.interfaces.fsl as fsl
import nipype.interfaces.mrtrix as mrtrix
import nipype.interfaces.ants as ants
import nipype.pipeline.engine as pe # pypeline engine
def calc_tpm_fn(tracks, template):
import os
from nipype import logging
from nipype.utils.filemanip import split_filename
path, name, ext = split_filename(tracks)
file_name = os.path.abspath(name + 'TPM.nii')
iflogger = logging.getLogger('interface')
iflogger.info(tracks)
iflogger.info(template)
import subprocess
iflogger.info(" ".join(["tracks2prob","-template",template,"-totallength", tracks, file_name]))
subprocess.call(["tracks2prob","-template",template,"-totallength", tracks, file_name])
return file_name
def binarize_image_fn(in_file):
import os.path as op
import nibabel as nb
from nipype.utils.filemanip import split_filename
path, in_name, ext = split_filename(in_file)
img = nb.load(in_file)
data = img.get_data()
data = data!=0
new_image = nb.Nifti1Image(data, header=img.get_header(), affine=img.get_affine())
out_file = op.abspath(in_name + '_bin.nii.gz')
nb.save(new_image, out_file)
return out_file
def clean_warp_field_fn(combined_warp_x, combined_warp_y, combined_warp_z, default_value):
import os.path as op
from nipype import logging
from nipype.utils.filemanip import split_filename
import nibabel as nb
import numpy as np
path, name, ext = split_filename(combined_warp_x)
out_file = op.abspath(name + 'CleanedWarp.nii')
iflogger = logging.getLogger('interface')
iflogger.info(default_value)
imgs = []
filenames = [combined_warp_x, combined_warp_y, combined_warp_z]
for fname in filenames:
img = nb.load(fname)
data = img.get_data()
data[data==default_value] = np.NaN
new_img = nb.Nifti1Image(data=data, header=img.get_header(), affine=img.get_affine())
imgs.append(new_img)
image4d = nb.concat_images(imgs, check_affines=True)
nb.save(image4d, out_file)
return out_file
def split_warp_volumes_fn(in_file):
from nipype import logging
from nipype.utils.filemanip import split_filename
import nibabel as nb
import os.path as op
iflogger = logging.getLogger('interface')
iflogger.info(in_file)
path, name, ext = split_filename(in_file)
image = nb.load(in_file)
x_img, y_img, z_img = nb.four_to_three(image)
x = op.abspath(name + '_x' + ".nii.gz")
y = op.abspath(name + '_y' + ".nii.gz")
z = op.abspath(name + '_z' + ".nii.gz")
nb.save(x_img, x)
nb.save(y_img, y)
nb.save(z_img, z)
return x, y, z
def create_track_normalization_pipeline(name="normtracks"):
inputnode = pe.Node(interface=util.IdentityInterface(fields=["tracks",
"inv_warp",
"affine",
"rigid",
"APM",
"template"]),
name="inputnode")
def_value = 123456
gen_unit_warpfield = pe.Node(
interface=mrtrix.GenerateUnitWarpField(), name='gen_unit_warpfield')
apply_transform_x = pe.Node(interface=ants.ApplyTransforms(), name='apply_transform_x')
apply_transform_x.inputs.dimension = 3
apply_transform_x.inputs.input_image_type = 0
apply_transform_x.inputs.default_value = def_value
apply_transform_x.inputs.invert_transform_flags = [False, True, False]
apply_transform_y = apply_transform_x.clone("apply_transform_y")
apply_transform_z = apply_transform_x.clone("apply_transform_z")
apply_transform_Test = apply_transform_x.clone("apply_transform_Test")
merge_transforms = pe.Node(util.Merge(3), name='merge_transforms')
split_volumes = pe.Node(name='split_volumes',
interface=Function(input_names=["in_file"],
output_names=['x', 'y', 'z'],
function=split_warp_volumes_fn))
clean_warp_field = pe.Node(name='clean_warp_field',
interface=Function(
input_names=["combined_warp_x",
"combined_warp_y", "combined_warp_z", "default_value"],
output_names=['out_file'],
function=clean_warp_field_fn))
clean_warp_field.inputs.default_value = def_value
binarize_image = pe.Node(name='binarize_image',
interface=Function(input_names=["in_file"],
output_names=['out_file'],
function=binarize_image_fn))
norm_tracks = pe.Node(
interface=mrtrix.NormalizeTracks(), name='norm_tracks')
tracks2tdi = pe.Node(interface=mrtrix.Tracks2Prob(),name='tracks2tdi')
filter_tracks = pe.Node(interface=mrtrix.FilterTracks(),name='filter_tracks')
calc_tpm = pe.Node(name='calc_tpm',
interface=Function(input_names=["tracks", "template"],
output_names=['tpm'],
function=calc_tpm_fn))
divide_tdi_by_tpm = pe.Node(interface=fsl.MultiImageMaths(), name="divide_tdi_by_tpm")
divide_tdi_by_tpm.inputs.op_string = "-div %s"
output_fields = ["normalized_cropped_tracks", "tpm", "tdi", "apm"]
outputnode = pe.Node(
interface=util.IdentityInterface(fields=output_fields),
name="outputnode")
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
workflow.connect(
[(inputnode, gen_unit_warpfield, [('template', 'in_file')])])
workflow.connect(
[(gen_unit_warpfield, split_volumes, [('out_file', 'in_file')])])
workflow.connect(
[(inputnode, merge_transforms, [('inv_warp', 'in3')])])
workflow.connect(
[(inputnode, merge_transforms, [('affine', 'in2')])])
workflow.connect(
[(inputnode, merge_transforms, [('rigid', 'in1')])])
workflow.connect(
[(inputnode, apply_transform_Test, [('template', 'input_image')])])
workflow.connect(
[(merge_transforms, apply_transform_Test, [('out', 'transforms')])])
workflow.connect(
[(inputnode, apply_transform_Test, [('APM', 'reference_image')])])
##### --------------- X ------------------
workflow.connect(
[(merge_transforms, apply_transform_x, [('out', 'transforms')])])
workflow.connect(
[(inputnode, apply_transform_x, [('APM', 'reference_image')])])
workflow.connect(
[(split_volumes, apply_transform_x, [('x', 'input_image')])])
##### --------------- Y ------------------
workflow.connect(
[(merge_transforms, apply_transform_y, [('out', 'transforms')])])
workflow.connect(
[(inputnode, apply_transform_y, [('APM', 'reference_image')])])
workflow.connect(
[(split_volumes, apply_transform_y, [('y', 'input_image')])])
##### --------------- Z ------------------
workflow.connect(
[(merge_transforms, apply_transform_z, [('out', 'transforms')])])
workflow.connect(
[(inputnode, apply_transform_z, [('APM', 'reference_image')])])
workflow.connect(
[(split_volumes, apply_transform_z, [('z', 'input_image')])])
##### --------------- Clean ------------------
workflow.connect(
[(apply_transform_x, clean_warp_field, [('output_image', 'combined_warp_x')])])
workflow.connect(
[(apply_transform_y, clean_warp_field, [('output_image', 'combined_warp_y')])])
workflow.connect(
[(apply_transform_z, clean_warp_field, [('output_image', 'combined_warp_z')])])
##### --------------- Normalise ------------------
workflow.connect([(inputnode, norm_tracks, [("tracks", "in_file")])])
workflow.connect(
[(clean_warp_field, norm_tracks, [('out_file', 'transform_image')])])
##### --------------- Mask tracks ------------------
# In the end this has no effect, since it is an inclusion filtering
# realistically we should have excluded tracks that went out of the mask.
workflow.connect(
[(inputnode, binarize_image, [("template", "in_file")])])
#workflow.connect(
# [(binarize_image, tracks2tdi, [("out_file", "template_file")])])
#workflow.connect(
# [(binarize_image, calc_tpm, [("out_file", "template")])])
tracks2tdi.inputs.template_file = "/media/EBSNorm/Brain_mask.nii"
calc_tpm.inputs.template = "/media/EBSNorm/Brain_mask.nii"
# -------------------
workflow.connect(
[(norm_tracks, filter_tracks, [("out_file", "in_file")])])
workflow.connect(
[(binarize_image, filter_tracks, [("out_file", "include_mask_image")])])
# -------------------# -------------------# -------------------
workflow.connect(
[(filter_tracks, calc_tpm, [("tracks", "tracks")])])
workflow.connect(
[(filter_tracks, tracks2tdi, [("tracks", "in_file")])])
#### --------------- Create TDI, TPM, and APM ------------------
workflow.connect([(tracks2tdi, divide_tdi_by_tpm,[('tract_image', 'operand_files')])])
workflow.connect([(calc_tpm, divide_tdi_by_tpm,[('tpm', 'in_file')])])
workflow.connect([(tracks2tdi, outputnode, [("tract_image", "tdi")])])
workflow.connect([(divide_tdi_by_tpm, outputnode, [("out_file", "apm")])])
workflow.connect([(calc_tpm, outputnode, [("tpm", "tpm")])])
workflow.connect(
[(filter_tracks, outputnode, [("tracks", "normalized_cropped_tracks")])])
return workflow
data_dir = op.abspath('/media/EBS/')
output_dir = op.abspath('/media/EBSNorm')
fsl.FSLCommand.set_default_output_type('NIFTI')
info = dict(tracks=[['subject_id', 'CSD_tracked']],
inv_warp=[['subject_id', 'InverseWarp']],
affine=[['subject_id', 'Affine']],
APM=[['subject_id', 'bmatrix_2500_CSD_trackedTPM_maths_rl']],
rigid=[['subject_id', 'InverseComposite']])
control_list = ['p07090', 'p07108',
'p07113', 'p07116', 'p07131', 'p07183', 'p07188', 'p07198',
'p07200', 'p07232', 'p07242', 'p07248', 'p07262', 'p07305',
'p07465', 'p07467', 'p07468', 'p07488', 'p07493', 'p07509',
'p07519', 'p07523', 'p07535', 'p07601', 'p07612', 'p07663']
patient_list = [
'p06316', 'p06871', 'p06873', 'p06889', 'p06890',
'p06891', 'p06904', 'p06905', 'p06933', 'p06940',
'p06941', 'p06968', 'p07091', 'p07109', 'p07153',
'p07155', 'p07258', 'p07276', 'p07594', 'p07599',
'p07602', 'p07611', 'p07616', 'p07618', 'p07677',
'p07685', 'p07194']
control_list.extend(patient_list)
#subject_list = ['p07090']
subject_list = control_list
infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', subject_list)
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=info.keys()),
name='datasource')
datasource.inputs.template = "%s/%s"
datasource.inputs.base_directory = data_dir
datasource.inputs.field_template = dict(
tracks='TDI_lmax8/parkflow_tdis/parkflow_tdis/_subject_id_%s/CSDstreamtrack/*%s',
inv_warp='TemplateGeneration/Step2_Warping/Deformed*%s_*%s.nii.gz',
affine='TemplateGeneration/Step2_Warping/Deformed*%s*%s.txt',
APM='TemplateGeneration/Step1_Affine/%s_%s.nii',
rigid='TemplateGeneration/Step1_Affine/%s_%s.h5')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
norm = create_track_normalization_pipeline("ANTSTrackNorm")
template_file = op.abspath('/media/EBS/TemplateGeneration/Step2_Warping/Deformed_template.nii.gz')
norm.inputs.inputnode.template = template_file
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = output_dir
graph = pe.Workflow(name='TrackNorm')
graph.base_dir = output_dir
graph.connect([(infosource, datasource, [('subject_id', 'subject_id')])])
graph.connect([(datasource, norm, [('tracks', 'inputnode.tracks')])])
graph.connect([(datasource, norm, [('inv_warp', 'inputnode.inv_warp')])])
graph.connect([(datasource, norm, [('affine', 'inputnode.affine')])])
graph.connect([(datasource, norm, [('rigid', 'inputnode.rigid')])])
graph.connect([(datasource, norm, [('APM', 'inputnode.APM')])])
#graph.connect([(infosource, datasink,[('subject_id','@subject_id')])])
from nipype import config
cfg = dict(logging=dict(workflow_level = 'DEBUG'),
execution={'remove_unnecessary_outputs': True})
config.update_config(cfg)
graph.run(plugin='MultiProc', plugin_args={'n_procs' : 32})
#updatehash=False) #