def inclusion_filtering_mrtrix3(track_file,
roi_file,
fa_file,
md_file,
roi_names=None,
registration_image_file=None,
registration_matrix_file=None,
prefix=None,
tdi_threshold=10):
import os
import os.path as op
import numpy as np
import glob
from coma.workflows.dmn import get_rois, save_heatmap
from coma.interfaces.dti import write_trackvis_scene
import nipype.pipeline.engine as pe
import nipype.interfaces.fsl as fsl
import nipype.interfaces.mrtrix as mrtrix
import nipype.interfaces.diffusion_toolkit as dtk
from nipype.utils.filemanip import split_filename
import subprocess
import shutil
rois = get_rois(roi_file)
fa_out_matrix = op.abspath("%s_FA.csv" % prefix)
md_out_matrix = op.abspath("%s_MD.csv" % prefix)
invLen_invVol_out_matrix = op.abspath("%s_invLen_invVol.csv" % prefix)
subprocess.call([
"tck2connectome", "-assignment_voxel_lookup", "-zero_diagonal",
"-metric", "mean_scalar", "-image", fa_file, track_file, roi_file,
fa_out_matrix
])
subprocess.call([
"tck2connectome", "-assignment_voxel_lookup", "-zero_diagonal",
"-metric", "mean_scalar", "-image", md_file, track_file, roi_file,
md_out_matrix
])
subprocess.call([
"tck2connectome", "-assignment_voxel_lookup", "-zero_diagonal",
"-metric", "invlength_invnodevolume", track_file, roi_file,
invLen_invVol_out_matrix
])
subprocess.call([
"tcknodeextract", "-assignment_voxel_lookup", track_file, roi_file,
prefix + "_"
])
fa_matrix_thr = np.zeros((len(rois), len(rois)))
md_matrix_thr = np.zeros((len(rois), len(rois)))
tdi_matrix = np.zeros((len(rois), len(rois)))
track_volume_matrix = np.zeros((len(rois), len(rois)))
out_files = []
track_files = []
for idx_i, roi_i in enumerate(rois):
for idx_j, roi_j in enumerate(rois):
if idx_j >= idx_i:
filtered_tracks = glob.glob(
op.abspath(prefix + "_%s-%s.tck" % (roi_i, roi_j)))[0]
print(filtered_tracks)
if roi_names is None:
roi_i = str(int(roi_i))
roi_j = str(int(roi_j))
idpair = "%s_%s" % (roi_i, roi_j)
idpair = idpair.replace(".", "-")
else:
roi_name_i = roi_names[idx_i]
roi_name_j = roi_names[idx_j]
idpair = "%s_%s" % (roi_name_i, roi_name_j)
tracks2tdi = pe.Node(interface=mrtrix.Tracks2Prob(),
name='tdi_%s' % idpair)
tracks2tdi.inputs.template_file = fa_file
tracks2tdi.inputs.in_file = filtered_tracks
out_tdi_name = op.abspath("%s_TDI_%s.nii.gz" %
(prefix, idpair))
tracks2tdi.inputs.out_filename = out_tdi_name
tracks2tdi.inputs.output_datatype = "Int16"
binarize_tdi = pe.Node(interface=fsl.ImageMaths(),
name='binarize_tdi_%s' % idpair)
binarize_tdi.inputs.op_string = "-thr %d -bin" % tdi_threshold
out_tdi_vol_name = op.abspath("%s_TDI_bin_%d_%s.nii.gz" %
(prefix, tdi_threshold, idpair))
binarize_tdi.inputs.out_file = out_tdi_vol_name
mask_fa = pe.Node(interface=fsl.MultiImageMaths(),
name='mask_fa_%s' % idpair)
mask_fa.inputs.op_string = "-mul %s"
mask_fa.inputs.operand_files = [fa_file]
out_fa_name = op.abspath("%s_FA_%s.nii.gz" % (prefix, idpair))
mask_fa.inputs.out_file = out_fa_name
mask_md = mask_fa.clone(name='mask_md_%s' % idpair)
mask_md.inputs.operand_files = [md_file]
out_md_name = op.abspath("%s_MD_%s.nii.gz" % (prefix, idpair))
mask_md.inputs.out_file = out_md_name
mean_fa = pe.Node(interface=fsl.ImageStats(op_string='-M'),
name='mean_fa_%s' % idpair)
mean_md = pe.Node(interface=fsl.ImageStats(op_string='-M'),
name='mean_md_%s' % idpair)
mean_tdi = pe.Node(interface=fsl.ImageStats(
op_string='-l %d -M' % tdi_threshold),
name='mean_tdi_%s' % idpair)
track_volume = pe.Node(interface=fsl.ImageStats(
op_string='-l %d -V' % tdi_threshold),
name='track_volume_%s' % idpair)
tck2trk = mrtrix.MRTrix2TrackVis()
tck2trk.inputs.image_file = fa_file
tck2trk.inputs.in_file = filtered_tracks
trk_file = op.abspath("%s_%s.trk" % (prefix, idpair))
tck2trk.inputs.out_filename = trk_file
tck2trk.base_dir = op.abspath(".")
if registration_image_file is not None and registration_matrix_file is not None:
tck2trk.inputs.registration_image_file = registration_image_file
tck2trk.inputs.matrix_file = registration_matrix_file
workflow = pe.Workflow(name=idpair)
workflow.base_dir = op.abspath(idpair)
workflow.connect([(tracks2tdi, binarize_tdi, [("tract_image",
"in_file")])])
workflow.connect([(binarize_tdi, mask_fa, [("out_file",
"in_file")])])
workflow.connect([(binarize_tdi, mask_md, [("out_file",
"in_file")])])
workflow.connect([(mask_fa, mean_fa, [("out_file", "in_file")])
])
workflow.connect([(mask_md, mean_md, [("out_file", "in_file")])
])
workflow.connect([(tracks2tdi, mean_tdi, [("tract_image",
"in_file")])])
workflow.connect([(tracks2tdi, track_volume, [("tract_image",
"in_file")])])
workflow.config['execution'] = {
'remove_unnecessary_outputs': 'false',
'hash_method': 'timestamp'
}
result = workflow.run()
tck2trk.run()
fa_masked = glob.glob(out_fa_name)[0]
md_masked = glob.glob(out_md_name)[0]
if roi_names is not None:
tracks = op.abspath(prefix + "_%s-%s.tck" %
(roi_name_i, roi_name_j))
shutil.move(filtered_tracks, tracks)
else:
tracks = filtered_tracks
tdi = glob.glob(out_tdi_vol_name)[0]
nodes = result.nodes()
node_names = [s.name for s in nodes]
mean_fa_node = [
nodes[idx] for idx, s in enumerate(node_names)
if "mean_fa" in s
][0]
mean_fa = mean_fa_node.result.outputs.out_stat
mean_md_node = [
nodes[idx] for idx, s in enumerate(node_names)
if "mean_md" in s
][0]
mean_md = mean_md_node.result.outputs.out_stat
mean_tdi_node = [
nodes[idx] for idx, s in enumerate(node_names)
if "mean_tdi" in s
][0]
mean_tdi = mean_tdi_node.result.outputs.out_stat
track_volume_node = [
nodes[idx] for idx, s in enumerate(node_names)
if "track_volume" in s
][0]
track_volume = track_volume_node.result.outputs.out_stat[
1] # First value is in voxels, 2nd is in volume
if track_volume == 0:
os.remove(fa_masked)
os.remove(md_masked)
os.remove(tdi)
else:
out_files.append(md_masked)
out_files.append(fa_masked)
out_files.append(tracks)
out_files.append(tdi)
if op.exists(trk_file):
out_files.append(trk_file)
track_files.append(trk_file)
assert (0 <= mean_fa < 1)
fa_matrix_thr[idx_i, idx_j] = mean_fa
md_matrix_thr[idx_i, idx_j] = mean_md
tdi_matrix[idx_i, idx_j] = mean_tdi
track_volume_matrix[idx_i, idx_j] = track_volume
fa_matrix = np.loadtxt(fa_out_matrix)
md_matrix = np.loadtxt(md_out_matrix)
fa_matrix = fa_matrix + fa_matrix.T
md_matrix = md_matrix + md_matrix.T
fa_matrix_thr = fa_matrix_thr + fa_matrix_thr.T
md_matrix_thr = md_matrix_thr + md_matrix_thr.T
tdi_matrix = tdi_matrix + tdi_matrix.T
invLen_invVol_matrix = np.loadtxt(invLen_invVol_out_matrix)
invLen_invVol_matrix = invLen_invVol_matrix + invLen_invVol_matrix.T
track_volume_matrix = track_volume_matrix + track_volume_matrix.T
if prefix is not None:
npz_data = op.abspath("%s_connectivity.npz" % prefix)
else:
_, prefix, _ = split_filename(track_file)
npz_data = op.abspath("%s_connectivity.npz" % prefix)
np.savez(npz_data,
fa=fa_matrix,
md=md_matrix,
tdi=tdi_matrix,
trkvol=track_volume_matrix,
fa_thr=fa_matrix_thr,
md_thr=md_matrix_thr,
invLen_invVol=invLen_invVol_matrix)
print("Saving heatmaps...")
fa_heatmap = save_heatmap(fa_matrix, roi_names, '%s_fa' % prefix)
fa_heatmap_thr = save_heatmap(fa_matrix_thr, roi_names,
'%s_fa_thr' % prefix)
md_heatmap = save_heatmap(md_matrix, roi_names, '%s_md' % prefix)
md_heatmap_thr = save_heatmap(md_matrix_thr, roi_names,
'%s_md_thr' % prefix)
tdi_heatmap = save_heatmap(tdi_matrix, roi_names, '%s_tdi' % prefix)
trk_vol_heatmap = save_heatmap(track_volume_matrix, roi_names,
'%s_trk_vol' % prefix)
invLen_invVol_heatmap = save_heatmap(invLen_invVol_matrix, roi_names,
'%s_invLen_invVol' % prefix)
summary_images = []
summary_images.append(fa_heatmap)
summary_images.append(fa_heatmap_thr)
summary_images.append(md_heatmap)
summary_images.append(md_heatmap_thr)
summary_images.append(tdi_heatmap)
summary_images.append(trk_vol_heatmap)
summary_images.append(invLen_invVol_heatmap)
out_merged_file = op.abspath('%s_MergedTracks.trk' % prefix)
skip = 80.
track_merge = pe.Node(interface=dtk.TrackMerge(), name='track_merge')
track_merge.inputs.track_files = track_files
track_merge.inputs.output_file = out_merged_file
track_merge.run()
track_names = []
for t in track_files:
_, name, _ = split_filename(t)
track_names.append(name)
out_scene = op.abspath("%s_MergedScene.scene" % prefix)
out_scene_file = write_trackvis_scene(out_merged_file,
n_clusters=len(track_files),
skip=skip,
names=track_names,
out_file=out_scene)
print("Merged track file written to %s" % out_merged_file)
print("Scene file written to %s" % out_scene_file)
out_files.append(out_merged_file)
out_files.append(out_scene_file)
return out_files, npz_data, summary_images
def inclusion_filtering(track_file, roi_file, fa_file, md_file, roi_names=None, registration_image_file=None, registration_matrix_file=None, prefix=None, tdi_threshold=10):
import os
import os.path as op
import numpy as np
import glob
from coma.workflows.dmn import split_roi, get_rois, save_heatmap
from coma.interfaces.dti import write_trackvis_scene
import nipype.pipeline.engine as pe
import nipype.interfaces.fsl as fsl
import nipype.interfaces.mrtrix as mrtrix
import nipype.interfaces.diffusion_toolkit as dtk
from nipype.utils.filemanip import split_filename
rois = get_rois(roi_file)
roi_files = split_roi(roi_file, roi_names, prefix)
out_files = []
fa_matrix = np.zeros((len(rois), len(rois)))
md_matrix = np.zeros((len(rois), len(rois)))
tdi_matrix = np.zeros((len(rois), len(rois)))
track_volume_matrix = np.zeros((len(rois), len(rois)))
track_files = []
for idx_i, roi_i in enumerate(rois):
if roi_names is None:
roi_i_file = [s for s in roi_files if "%d" % roi_i in s]
roi_i = str(int(roi_i))
else:
roi_i_file = [s for s in roi_files if "%s" % roi_names[idx_i] in s]
roi_i = roi_names[idx_i]
filter_tracks_roi_i = pe.Node(interface=mrtrix.FilterTracks(), name='filt_%s' % roi_i)
filter_tracks_roi_i.inputs.in_file = track_file
filter_tracks_roi_i.inputs.include_file = roi_i_file[0]
out_roi_i_filtered = op.abspath("%s_FiltTracks_%s.tck" % (prefix, roi_i))
filter_tracks_roi_i.inputs.out_filename = out_roi_i_filtered
filter_tracks_roi_i.run()
for idx_j, roi_j in enumerate(rois):
if idx_j > idx_i:
if roi_names is None:
roi_j = str(int(roi_j))
idpair = "%s_%s" % (roi_i, roi_j)
idpair = idpair.replace(".","-")
roi_j_file = [s for s in roi_files if "%s" % roi_j in s]
else:
roi_j = roi_names[idx_j]
roi_j_file = [s for s in roi_files if "%s" % roi_names[idx_j] in s]
idpair = "%s_%s" % (roi_i, roi_j)
filter_tracks_roi_i_roi_j = pe.Node(interface=mrtrix.FilterTracks(), name='filt_%s' % idpair)
filter_tracks_roi_i_roi_j.inputs.in_file = out_roi_i_filtered
filter_tracks_roi_i_roi_j.inputs.include_file = roi_j_file[0]
filter_tracks_roi_i_roi_j.inputs.out_filename = "%s_FiltTracks_%s.tck" % (prefix, idpair)
tracks2tdi = pe.Node(interface=mrtrix.Tracks2Prob(), name='tdi_%s' % idpair)
tracks2tdi.inputs.template_file = fa_file
out_tdi_name = op.abspath("%s_TDI_%s.nii.gz" % (prefix, idpair))
tracks2tdi.inputs.out_filename = out_tdi_name
tracks2tdi.inputs.output_datatype = "Int16"
binarize_tdi = pe.Node(interface=fsl.ImageMaths(), name='binarize_tdi_%s' % idpair)
binarize_tdi.inputs.op_string = "-thr %d -bin" % tdi_threshold
out_tdi_vol_name = op.abspath("%s_TDI_bin_%d_%s.nii.gz" % (prefix, tdi_threshold, idpair))
binarize_tdi.inputs.out_file = out_tdi_vol_name
mask_fa = pe.Node(interface=fsl.MultiImageMaths(), name='mask_fa_%s' % idpair)
mask_fa.inputs.op_string = "-mul %s"
mask_fa.inputs.operand_files = [fa_file]
out_fa_name = op.abspath("%s_FA_%s.nii.gz" % (prefix, idpair))
mask_fa.inputs.out_file = out_fa_name
mask_md = mask_fa.clone(name='mask_md_%s' % idpair)
mask_md.inputs.operand_files = [md_file]
out_md_name = op.abspath("%s_MD_%s.nii.gz" % (prefix, idpair))
mask_md.inputs.out_file = out_md_name
mean_fa = pe.Node(interface=fsl.ImageStats(op_string = '-M'), name = 'mean_fa_%s' % idpair)
mean_md = pe.Node(interface=fsl.ImageStats(op_string = '-M'), name = 'mean_md_%s' % idpair)
mean_tdi = pe.Node(interface=fsl.ImageStats(op_string = '-l %d -M' % tdi_threshold), name = 'mean_tdi_%s' % idpair)
track_volume = pe.Node(interface=fsl.ImageStats(op_string = '-l %d -V' % tdi_threshold), name = 'track_volume_%s' % idpair)
tck2trk = pe.Node(interface=mrtrix.MRTrix2TrackVis(), name='tck2trk')
tck2trk.inputs.image_file = fa_file
tck2trk.inputs.out_filename = idpair + ".trk"
if registration_image_file is not None and registration_matrix_file is not None:
tck2trk.inputs.registration_image_file = registration_image_file
tck2trk.inputs.matrix_file = registration_matrix_file
workflow = pe.Workflow(name=idpair)
workflow.base_dir = op.abspath(idpair)
workflow.connect(
[(filter_tracks_roi_i_roi_j, tracks2tdi, [("out_file", "in_file")])])
workflow.connect(
[(filter_tracks_roi_i_roi_j, tck2trk, [("out_file", "in_file")])])
workflow.connect(
[(tracks2tdi, binarize_tdi, [("tract_image", "in_file")])])
workflow.connect(
[(binarize_tdi, mask_fa, [("out_file", "in_file")])])
workflow.connect(
[(binarize_tdi, mask_md, [("out_file", "in_file")])])
workflow.connect(
[(mask_fa, mean_fa, [("out_file", "in_file")])])
workflow.connect(
[(mask_md, mean_md, [("out_file", "in_file")])])
workflow.connect(
[(tracks2tdi, mean_tdi, [("tract_image", "in_file")])])
workflow.connect(
[(tracks2tdi, track_volume, [("tract_image", "in_file")])])
workflow.config['execution'] = {'remove_unnecessary_outputs': 'false',
'hash_method': 'timestamp'}
result = workflow.run()
fa_masked = glob.glob(out_fa_name)[0]
md_masked = glob.glob(out_md_name)[0]
tracks = glob.glob(op.abspath(op.join(idpair,idpair,'filt_%s' % idpair, "%s_FiltTracks_%s.tck" % (prefix, idpair))))[0]
tdi = glob.glob(out_tdi_vol_name)[0]
nodes = result.nodes()
node_names = [s.name for s in nodes]
mean_fa_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_fa" in s][0]
mean_fa = mean_fa_node.result.outputs.out_stat
mean_md_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_md" in s][0]
mean_md = mean_md_node.result.outputs.out_stat
mean_tdi_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_tdi" in s][0]
mean_tdi = mean_tdi_node.result.outputs.out_stat
track_volume_node = [nodes[idx] for idx, s in enumerate(node_names) if "track_volume" in s][0]
track_volume = track_volume_node.result.outputs.out_stat[1] # First value is in voxels, 2nd is in volume
trk_node = [nodes[idx] for idx, s in enumerate(node_names) if "tck2trk" in s][0]
trk_file = trk_node.result.outputs.out_file
if track_volume == 0:
os.remove(fa_masked)
os.remove(md_masked)
os.remove(tdi)
os.remove(tracks)
else:
out_files.append(md_masked)
out_files.append(fa_masked)
out_files.append(tracks)
out_files.append(tdi)
out_files.append(trk_file)
track_files.append(trk_file)
assert(0 <= mean_fa < 1)
fa_matrix[idx_i, idx_j] = mean_fa
md_matrix[idx_i, idx_j] = mean_md
tdi_matrix[idx_i, idx_j] = mean_tdi
track_volume_matrix[idx_i, idx_j] = track_volume
fa_matrix = fa_matrix + fa_matrix.T
md_matrix = md_matrix + md_matrix.T
tdi_matrix = tdi_matrix + tdi_matrix.T
track_volume_matrix = track_volume_matrix + track_volume_matrix.T
if prefix is not None:
npz_data = op.abspath("%s_connectivity.npz" % prefix)
else:
_, prefix, _ = split_filename(track_file)
npz_data = op.abspath("%s_connectivity.npz" % prefix)
np.savez(npz_data, fa=fa_matrix, md=md_matrix, tdi=tdi_matrix, trkvol=track_volume_matrix)
print("Saving heatmaps...")
fa_heatmap = save_heatmap(fa_matrix, roi_names, '%s_fa' % prefix)
md_heatmap = save_heatmap(md_matrix, roi_names, '%s_md' % prefix)
tdi_heatmap = save_heatmap(tdi_matrix, roi_names, '%s_tdi' % prefix)
trk_vol_heatmap = save_heatmap(track_volume_matrix, roi_names, '%s_trk_vol' % prefix)
summary_images = []
summary_images.append(fa_heatmap)
summary_images.append(md_heatmap)
summary_images.append(tdi_heatmap)
summary_images.append(trk_vol_heatmap)
out_merged_file = op.abspath('%s_MergedTracks.trk' % prefix)
skip = 80.
track_merge = pe.Node(interface=dtk.TrackMerge(), name='track_merge')
track_merge.inputs.track_files = track_files
track_merge.inputs.output_file = out_merged_file
track_merge.run()
track_names = []
for t in track_files:
_, name, _ = split_filename(t)
track_names.append(name)
out_scene = op.abspath("%s_MergedScene.scene" % prefix)
out_scene_file = write_trackvis_scene(out_merged_file, n_clusters=len(track_files), skip=skip, names=track_names, out_file=out_scene)
print("Merged track file written to %s" % out_merged_file)
print("Scene file written to %s" % out_scene_file)
out_files.append(out_merged_file)
out_files.append(out_scene_file)
return out_files, npz_data, summary_images
def inclusion_filtering_mrtrix3(track_file, roi_file, fa_file, md_file, roi_names=None, registration_image_file=None, registration_matrix_file=None, prefix=None, tdi_threshold=10):
import os
import os.path as op
import numpy as np
import glob
from coma.workflows.dmn import get_rois, save_heatmap
from coma.interfaces.dti import write_trackvis_scene
import nipype.pipeline.engine as pe
import nipype.interfaces.fsl as fsl
import nipype.interfaces.mrtrix as mrtrix
import nipype.interfaces.diffusion_toolkit as dtk
from nipype.utils.filemanip import split_filename
import subprocess
import shutil
rois = get_rois(roi_file)
fa_out_matrix = op.abspath("%s_FA.csv" % prefix)
md_out_matrix = op.abspath("%s_MD.csv" % prefix)
invLen_invVol_out_matrix = op.abspath("%s_invLen_invVol.csv" % prefix)
subprocess.call(["tck2connectome", "-assignment_voxel_lookup",
"-zero_diagonal",
"-metric", "mean_scalar", "-image", fa_file,
track_file, roi_file, fa_out_matrix])
subprocess.call(["tck2connectome", "-assignment_voxel_lookup",
"-zero_diagonal",
"-metric", "mean_scalar", "-image", md_file,
track_file, roi_file, md_out_matrix])
subprocess.call(["tck2connectome", "-assignment_voxel_lookup",
"-zero_diagonal",
"-metric", "invlength_invnodevolume",
track_file, roi_file, invLen_invVol_out_matrix])
subprocess.call(["tcknodeextract", "-assignment_voxel_lookup",
track_file, roi_file, prefix + "_"])
fa_matrix_thr = np.zeros((len(rois), len(rois)))
md_matrix_thr = np.zeros((len(rois), len(rois)))
tdi_matrix = np.zeros((len(rois), len(rois)))
track_volume_matrix = np.zeros((len(rois), len(rois)))
out_files = []
track_files = []
for idx_i, roi_i in enumerate(rois):
for idx_j, roi_j in enumerate(rois):
if idx_j >= idx_i:
filtered_tracks = glob.glob(op.abspath(prefix + "_%s-%s.tck" % (roi_i, roi_j)))[0]
print(filtered_tracks)
if roi_names is None:
roi_i = str(int(roi_i))
roi_j = str(int(roi_j))
idpair = "%s_%s" % (roi_i, roi_j)
idpair = idpair.replace(".","-")
else:
roi_name_i = roi_names[idx_i]
roi_name_j = roi_names[idx_j]
idpair = "%s_%s" % (roi_name_i, roi_name_j)
tracks2tdi = pe.Node(interface=mrtrix.Tracks2Prob(), name='tdi_%s' % idpair)
tracks2tdi.inputs.template_file = fa_file
tracks2tdi.inputs.in_file = filtered_tracks
out_tdi_name = op.abspath("%s_TDI_%s.nii.gz" % (prefix, idpair))
tracks2tdi.inputs.out_filename = out_tdi_name
tracks2tdi.inputs.output_datatype = "Int16"
binarize_tdi = pe.Node(interface=fsl.ImageMaths(), name='binarize_tdi_%s' % idpair)
binarize_tdi.inputs.op_string = "-thr %d -bin" % tdi_threshold
out_tdi_vol_name = op.abspath("%s_TDI_bin_%d_%s.nii.gz" % (prefix, tdi_threshold, idpair))
binarize_tdi.inputs.out_file = out_tdi_vol_name
mask_fa = pe.Node(interface=fsl.MultiImageMaths(), name='mask_fa_%s' % idpair)
mask_fa.inputs.op_string = "-mul %s"
mask_fa.inputs.operand_files = [fa_file]
out_fa_name = op.abspath("%s_FA_%s.nii.gz" % (prefix, idpair))
mask_fa.inputs.out_file = out_fa_name
mask_md = mask_fa.clone(name='mask_md_%s' % idpair)
mask_md.inputs.operand_files = [md_file]
out_md_name = op.abspath("%s_MD_%s.nii.gz" % (prefix, idpair))
mask_md.inputs.out_file = out_md_name
mean_fa = pe.Node(interface=fsl.ImageStats(op_string = '-M'), name = 'mean_fa_%s' % idpair)
mean_md = pe.Node(interface=fsl.ImageStats(op_string = '-M'), name = 'mean_md_%s' % idpair)
mean_tdi = pe.Node(interface=fsl.ImageStats(op_string = '-l %d -M' % tdi_threshold), name = 'mean_tdi_%s' % idpair)
track_volume = pe.Node(interface=fsl.ImageStats(op_string = '-l %d -V' % tdi_threshold), name = 'track_volume_%s' % idpair)
tck2trk = mrtrix.MRTrix2TrackVis()
tck2trk.inputs.image_file = fa_file
tck2trk.inputs.in_file = filtered_tracks
trk_file = op.abspath("%s_%s.trk" % (prefix, idpair))
tck2trk.inputs.out_filename = trk_file
tck2trk.base_dir = op.abspath(".")
if registration_image_file is not None and registration_matrix_file is not None:
tck2trk.inputs.registration_image_file = registration_image_file
tck2trk.inputs.matrix_file = registration_matrix_file
workflow = pe.Workflow(name=idpair)
workflow.base_dir = op.abspath(idpair)
workflow.connect(
[(tracks2tdi, binarize_tdi, [("tract_image", "in_file")])])
workflow.connect(
[(binarize_tdi, mask_fa, [("out_file", "in_file")])])
workflow.connect(
[(binarize_tdi, mask_md, [("out_file", "in_file")])])
workflow.connect(
[(mask_fa, mean_fa, [("out_file", "in_file")])])
workflow.connect(
[(mask_md, mean_md, [("out_file", "in_file")])])
workflow.connect(
[(tracks2tdi, mean_tdi, [("tract_image", "in_file")])])
workflow.connect(
[(tracks2tdi, track_volume, [("tract_image", "in_file")])])
workflow.config['execution'] = {'remove_unnecessary_outputs': 'false',
'hash_method': 'timestamp'}
result = workflow.run()
tck2trk.run()
fa_masked = glob.glob(out_fa_name)[0]
md_masked = glob.glob(out_md_name)[0]
if roi_names is not None:
tracks = op.abspath(prefix + "_%s-%s.tck" % (roi_name_i, roi_name_j))
shutil.move(filtered_tracks, tracks)
else:
tracks = filtered_tracks
tdi = glob.glob(out_tdi_vol_name)[0]
nodes = result.nodes()
node_names = [s.name for s in nodes]
mean_fa_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_fa" in s][0]
mean_fa = mean_fa_node.result.outputs.out_stat
mean_md_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_md" in s][0]
mean_md = mean_md_node.result.outputs.out_stat
mean_tdi_node = [nodes[idx] for idx, s in enumerate(node_names) if "mean_tdi" in s][0]
mean_tdi = mean_tdi_node.result.outputs.out_stat
track_volume_node = [nodes[idx] for idx, s in enumerate(node_names) if "track_volume" in s][0]
track_volume = track_volume_node.result.outputs.out_stat[1] # First value is in voxels, 2nd is in volume
if track_volume == 0:
os.remove(fa_masked)
os.remove(md_masked)
os.remove(tdi)
else:
out_files.append(md_masked)
out_files.append(fa_masked)
out_files.append(tracks)
out_files.append(tdi)
if op.exists(trk_file):
out_files.append(trk_file)
track_files.append(trk_file)
assert(0 <= mean_fa < 1)
fa_matrix_thr[idx_i, idx_j] = mean_fa
md_matrix_thr[idx_i, idx_j] = mean_md
tdi_matrix[idx_i, idx_j] = mean_tdi
track_volume_matrix[idx_i, idx_j] = track_volume
fa_matrix = np.loadtxt(fa_out_matrix)
md_matrix = np.loadtxt(md_out_matrix)
fa_matrix = fa_matrix + fa_matrix.T
md_matrix = md_matrix + md_matrix.T
fa_matrix_thr = fa_matrix_thr + fa_matrix_thr.T
md_matrix_thr = md_matrix_thr + md_matrix_thr.T
tdi_matrix = tdi_matrix + tdi_matrix.T
invLen_invVol_matrix = np.loadtxt(invLen_invVol_out_matrix)
invLen_invVol_matrix = invLen_invVol_matrix + invLen_invVol_matrix.T
track_volume_matrix = track_volume_matrix + track_volume_matrix.T
if prefix is not None:
npz_data = op.abspath("%s_connectivity.npz" % prefix)
else:
_, prefix, _ = split_filename(track_file)
npz_data = op.abspath("%s_connectivity.npz" % prefix)
np.savez(npz_data, fa=fa_matrix, md=md_matrix, tdi=tdi_matrix, trkvol=track_volume_matrix,
fa_thr=fa_matrix_thr, md_thr=md_matrix_thr, invLen_invVol=invLen_invVol_matrix)
print("Saving heatmaps...")
fa_heatmap = save_heatmap(fa_matrix, roi_names, '%s_fa' % prefix)
fa_heatmap_thr = save_heatmap(fa_matrix_thr, roi_names, '%s_fa_thr' % prefix)
md_heatmap = save_heatmap(md_matrix, roi_names, '%s_md' % prefix)
md_heatmap_thr = save_heatmap(md_matrix_thr, roi_names, '%s_md_thr' % prefix)
tdi_heatmap = save_heatmap(tdi_matrix, roi_names, '%s_tdi' % prefix)
trk_vol_heatmap = save_heatmap(track_volume_matrix, roi_names, '%s_trk_vol' % prefix)
invLen_invVol_heatmap = save_heatmap(invLen_invVol_matrix, roi_names, '%s_invLen_invVol' % prefix)
summary_images = []
summary_images.append(fa_heatmap)
summary_images.append(fa_heatmap_thr)
summary_images.append(md_heatmap)
summary_images.append(md_heatmap_thr)
summary_images.append(tdi_heatmap)
summary_images.append(trk_vol_heatmap)
summary_images.append(invLen_invVol_heatmap)
out_merged_file = op.abspath('%s_MergedTracks.trk' % prefix)
skip = 80.
track_merge = pe.Node(interface=dtk.TrackMerge(), name='track_merge')
track_merge.inputs.track_files = track_files
track_merge.inputs.output_file = out_merged_file
track_merge.run()
track_names = []
for t in track_files:
_, name, _ = split_filename(t)
track_names.append(name)
out_scene = op.abspath("%s_MergedScene.scene" % prefix)
out_scene_file = write_trackvis_scene(out_merged_file, n_clusters=len(track_files), skip=skip, names=track_names, out_file=out_scene)
print("Merged track file written to %s" % out_merged_file)
print("Scene file written to %s" % out_scene_file)
out_files.append(out_merged_file)
out_files.append(out_scene_file)
return out_files, npz_data, summary_images
