def _run_interface(self, runtime):
tracks, header = trk.read(self.inputs.in_file)
if not isdefined(self.inputs.data_dims):
data_dims = header['dim']
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header['voxel_size']
else:
voxel_size = self.inputs.voxel_dims
affine = header['vox_to_ras']
streams = ((ii[0]) for ii in tracks)
data = density_map(streams, data_dims, voxel_size)
if data.max() < 2**15:
data = data.astype('int16')
img = nb.Nifti1Image(data,affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
iflogger.info('Track density map saved as {i}'.format(i=out_file))
iflogger.info('Data Dimensions {d}'.format(d=data_dims))
iflogger.info('Voxel Dimensions {v}'.format(v=voxel_size))
return runtime
def _run_interface(self, runtime):
tracks, header = trk.read(self.inputs.in_file)
if not isdefined(self.inputs.data_dims):
data_dims = header['dim']
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header['voxel_size']
else:
voxel_size = self.inputs.voxel_dims
affine = header['vox_to_ras']
streams = ((ii[0]) for ii in tracks)
data = density_map(streams, data_dims, voxel_size)
if data.max() < 2**15:
data = data.astype('int16')
img = nb.Nifti1Image(data, affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
iflogger.info('Track density map saved as {i}'.format(i=out_file))
iflogger.info('Data Dimensions {d}'.format(d=data_dims))
iflogger.info('Voxel Dimensions {v}'.format(v=voxel_size))
return runtime
def _run_interface(self, runtime):
# Loading the ROI file
import nibabel as nib
import numpy as np
from dipy.tracking import utils
img = nib.load(self.inputs.ROI_file)
data = img.get_data()
affine = img.get_affine()
# Getting the FA file
img = nib.load(self.inputs.FA_file)
FA_data = img.get_data()
FA_affine = img.get_affine()
# Loading the streamlines
from nibabel import trackvis
streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
streamlines = [s[0] for s in streams]
streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)
# Checking for negative values
from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
endpoints = [sl[0::len(sl)-1] for sl in streamlines]
lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
inds = np.dot(endpoints, lin_T)
inds += offset
negative_values = np.where(inds <0)[0]
for negative_value in sorted(negative_values, reverse=True):
del streamlines[negative_value]
# Constructing the streamlines matrix
matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
matrix[matrix < 10] = 0
# Constructing the FA matrix
dimensions = matrix.shape
FA_matrix = np.empty(shape=dimensions)
for i in range(0,dimensions[0]):
for j in range(0,dimensions[1]):
if matrix[i,j]:
dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
FA_matrix[i,j] = np.mean(FA_data[dm>0])
else:
FA_matrix[i,j] = 0
FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)
from nipype.utils.filemanip import split_filename
_, base, _ = split_filename(self.inputs.trackfile)
np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
return runtime
def test_density_map():
# One streamline diagonal in volume
streamlines = [np.array([np.arange(10)] * 3).T]
shape = (10, 10, 10)
x = np.arange(10)
expected = np.zeros(shape)
expected[x, x, x] = 1.
dm = density_map(streamlines, np.eye(4), shape)
npt.assert_array_equal(dm, expected)
# add streamline, make voxel_size smaller. Each streamline should only be
# counted once, even if multiple points lie in a voxel
streamlines.append(np.ones((5, 3)))
shape = (5, 5, 5)
x = np.arange(5)
expected = np.zeros(shape)
expected[x, x, x] = 1.
expected[0, 0, 0] += 1
affine = np.eye(4) * 2
affine[:3, 3] = 0.05
dm = density_map(streamlines, affine, shape)
npt.assert_array_equal(dm, expected)
# should work with a generator
dm = density_map(iter(streamlines), affine, shape)
npt.assert_array_equal(dm, expected)
# Test passing affine
affine = np.diag([2, 2, 2, 1.])
affine[:3, 3] = 1.
dm = density_map(streamlines, affine, shape)
npt.assert_array_equal(dm, expected)
# Shift the image by 2 voxels, ie 4mm
affine[:3, 3] -= 4.
expected_old = expected
new_shape = [i + 2 for i in shape]
expected = np.zeros(new_shape)
expected[2:, 2:, 2:] = expected_old
dm = density_map(streamlines, affine, new_shape)
npt.assert_array_equal(dm, expected)
def test_density_map():
# One streamline diagonal in volume
streamlines = [np.array([np.arange(10)]*3).T]
shape = (10, 10, 10)
x = np.arange(10)
expected = np.zeros(shape)
expected[x, x, x] = 1.
dm = density_map(streamlines, vol_dims=shape, voxel_size=(1, 1, 1))
assert_array_equal(dm, expected)
# add streamline, make voxel_size smaller. Each streamline should only be
# counted once, even if multiple points lie in a voxel
streamlines.append(np.ones((5, 3)))
shape = (5, 5, 5)
x = np.arange(5)
expected = np.zeros(shape)
expected[x, x, x] = 1.
expected[0, 0, 0] += 1
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
# should work with a generator
dm = density_map(iter(streamlines), vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
# Test passing affine
affine = np.diag([2, 2, 2, 1.])
affine[:3, 3] = 1.
dm = density_map(streamlines, shape, affine=affine)
assert_array_equal(dm, expected)
# Shift the image by 2 voxels, ie 4mm
affine[:3, 3] -= 4.
expected_old = expected
new_shape = [i + 2 for i in shape]
expected = np.zeros(new_shape)
expected[2:, 2:, 2:] = expected_old
dm = density_map(streamlines, new_shape, affine=affine)
assert_array_equal(dm, expected)
def create_tract_mask(trk_file_path, mask_output_path, ref_img_path, hole_closing=0, blob_th=10):
"""Adapted from https://github.com/MIC-DKFZ/TractSeg/issues/39#issuecomment-496181262
Creates binary mask from streamlines in .trk file.
Args:
trk_file_path: Path for the .trk file
mask_output_path: Path to save the binary mask.
ref_img_path: Path to the reference image to get affine and shape
hole_closing: Integer for closing the holes. (Default value = 0)
blob_th: Threshold for removing small blobs. (Default value = 10)
Returns:
None
"""
ref_img = nib.load(ref_img_path)
ref_affine = ref_img.affine
ref_shape = ref_img.shape
streamlines = nib.streamlines.load(trk_file_path).streamlines
# Upsample Streamlines (very important, especially when using DensityMap Threshold. Without upsampling eroded
# results)
print("Upsampling...")
print("Nr of points before upsampling " + str(get_number_of_points(streamlines)))
max_seq_len = abs(ref_affine[0, 0] / 4)
print("max_seq_len: {}".format(max_seq_len))
streamlines = list(utils_trk.subsegment(streamlines, max_seq_len))
print("Nr of points after upsampling " + str(get_number_of_points(streamlines)))
# Remember: Does not count if a fibers has no node inside of a voxel -> upsampling helps, but not perfect
# Counts the number of unique streamlines that pass through each voxel -> oversampling does not distort result
dm = utils_trk.density_map(streamlines, ref_shape, affine=ref_affine)
# Create Binary Map
dm_binary = dm > 1 # Using higher Threshold problematic, because often very sparse
dm_binary_c = dm_binary
# Filter Blobs
dm_binary_c = remove_small_blobs(dm_binary_c, threshold=blob_th)
# Closing of Holes (not ideal because tends to remove valid holes, e.g. in MCP)
if hole_closing > 0:
size = hole_closing
dm_binary_c = ndimage.binary_closing(dm_binary_c, structure=np.ones((size, size, size))).astype(dm_binary.dtype)
# Save Binary Mask
dm_binary_img = nib.Nifti1Image(dm_binary_c.astype("uint8"), ref_affine)
nib.save(dm_binary_img, mask_output_path)
def test_density_map():
#One streamline diagonal in volume
streamlines = [np.array([np.arange(10)]*3).T]
shape = (10, 10, 10)
x = np.arange(10)
expected = np.zeros(shape)
expected[x, x, x] = 1.
dm = density_map(streamlines, vol_dims=shape, voxel_size=(1, 1, 1))
assert_array_equal(dm, expected)
#add streamline, make voxel_size smaller. Each streamline should only be
#counted once, even if multiple points lie in a voxel
streamlines.append(np.ones((5, 3)))
shape = (5, 5, 5)
x = np.arange(5)
expected = np.zeros(shape)
expected[x, x, x] = 1.
expected[0, 0, 0] += 1
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
#should work with a generator
streamlines = iter(streamlines)
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
def test_density_map():
#One streamline diagonal in volume
streamlines = [np.array([np.arange(10)] * 3).T]
shape = (10, 10, 10)
x = np.arange(10)
expected = np.zeros(shape)
expected[x, x, x] = 1.
dm = density_map(streamlines, vol_dims=shape, voxel_size=(1, 1, 1))
assert_array_equal(dm, expected)
#add streamline, make voxel_size smaller. Each streamline should only be
#counted once, even if multiple points lie in a voxel
streamlines.append(np.ones((5, 3)))
shape = (5, 5, 5)
x = np.arange(5)
expected = np.zeros(shape)
expected[x, x, x] = 1.
expected[0, 0, 0] += 1
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
#should work with a generator
streamlines = iter(streamlines)
dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))
assert_array_equal(dm, expected)
def gen_dens(tracts, ref_vol, out_dens):
print('reading sta streamlines')
streams, hdr = trackvis.read(tracts)
streamlines = [s[0] for s in streams]
print('reading reference volume')
niihdr = nib.load(ref_vol)
nii = niihdr.get_data()
print('generating density map from tracts')
dm = utils.density_map(streamlines, affine=niihdr.affine, vol_dims=nii.shape)
dm_img = nib.Nifti1Image(dm.astype("uint16"), niihdr.affine)
print('saving tracts map')
dm_img.to_filename(out_dens)
def fib_density_map(volume, fiber, output):
'''
fiber density map
:param volume: structure image T1w
:param fiber: tck file
:param output: density map file
:return:
'''
shape = volume.shape
affine = volume.affine
streamstck = fiber.streamlines
image_volume = ditu.density_map(streamstck,
vol_dims=shape,
voxel_size=0.625,
affine=affine)
dm_img = nib.Nifti1Image(image_volume.astype("int16"), affine)
dm_img.to_filename(output)
def _run_interface(self, runtime):
from numpy import min_scalar_type
from dipy.tracking.utils import density_map
import nibabel.trackvis as nbt
tracks, header = nbt.read(self.inputs.in_file)
streams = ((ii[0]) for ii in tracks)
if isdefined(self.inputs.reference):
refnii = nb.load(self.inputs.reference)
affine = refnii.affine
data_dims = refnii.shape[:3]
kwargs = dict(affine=affine)
else:
IFLOGGER.warning(
"voxel_dims and data_dims are deprecated as of dipy "
"0.7.1. Please use reference input instead")
if not isdefined(self.inputs.data_dims):
data_dims = header["dim"]
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header["voxel_size"]
else:
voxel_size = self.inputs.voxel_dims
affine = header["vox_to_ras"]
kwargs = dict(voxel_size=voxel_size)
data = density_map(streams, data_dims, **kwargs)
data = data.astype(min_scalar_type(data.max()))
img = nb.Nifti1Image(data, affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
IFLOGGER.info(
"Track density map saved as %s, size=%s, dimensions=%s",
out_file,
img.shape,
img.header.get_zooms(),
)
return runtime
def tract_to_binary(file_in: str, file_out: str, ref_img_path: str):
HOLE_CLOSING = 0
# choose from "trk" or "trk_legacy"
# Use "trk_legacy" for zenodo dataset v1.1.0 and below
# Use "trk" for zenodo dataset v1.2.0
tracking_format = "trk"
ref_img = nib.load(ref_img_path)
ref_affine = ref_img.get_affine()
ref_shape = ref_img.get_data().shape
streams, hdr = trackvis.read(file_in)
streamlines = [s[0] for s in streams] # list of 2d ndarrays
if tracking_format == "trk_legacy":
streams, hdr = trackvis.read(file_in)
streamlines = [s[0] for s in streams]
else:
sl_file = nib.streamlines.load(file_in)
streamlines = sl_file.streamlines
# Upsample Streamlines (very important, especially when using DensityMap Threshold. Without upsampling eroded results)
max_seq_len = abs(ref_affine[0, 0] / 4)
streamlines = list(utils_trk.subsegment(streamlines, max_seq_len))
# Remember: Does not count if a fibers has no node inside of a voxel -> upsampling helps, but not perfect
# Counts the number of unique streamlines that pass through each voxel -> oversampling does not distort result
dm = utils_trk.density_map(streamlines, ref_affine, ref_shape)
# Create Binary Map
dm_binary = dm > 0 # Using higher Threshold problematic, because tends to remove valid parts (sparse fibers)
dm_binary_c = dm_binary
# Filter Blobs (might remove valid parts) -> do not use
# dm_binary_c = remove_small_blobs(dm_binary_c, threshold=10)
# Closing of Holes (not ideal because tends to remove valid holes, e.g. in MCP) -> do not use
# size = 1
# dm_binary_c = ndimage.binary_closing(dm_binary_c, structure=np.ones((size, size, size))).astype(dm_binary.dtype)
# Save Binary Mask
dm_binary_img = nib.Nifti1Image(dm_binary_c.astype("uint8"), ref_affine)
nib.save(dm_binary_img, file_out)
def make_sub_cnxn_visitation_map(sub,dpy_file,parc_file,warp_file,ref_file,
cnxn_inds_file,cnxn_name,vismap_fstr):
overwrite=True
import os,h5py,numpy as np,nibabel as nib
from dipy.io import Dpy
from dipy.tracking.utils import connectivity_matrix,length,density_map
from nipype.interfaces.fsl import ApplyWarp
F = h5py.File(cnxn_inds_file, 'r')
if cnxn_name in F.keys():
stream_idxs = F[cnxn_name].value
F.close()
D = Dpy(dpy_file, 'r')
streams = D.read_tracksi(stream_idxs)
D.close()
parc_img = nib.load(parc_file)
affine = np.eye(4)
outfile_nat = os.path.abspath('vismap_nat_%s.nii.gz' %cnxn_name)
outfile_std = os.path.abspath(vismap_fstr %cnxn_name)
dm = density_map(streams,parc_img.shape,affine=affine)
dm_img = nib.Nifti1Image(dm.astype("int16"), parc_img.affine)
dm_img.to_filename(outfile_nat)
print 'warping cnxn image to standard space'
aw = ApplyWarp(in_file=outfile_nat,
out_file=outfile_std,
ref_file =ref_file,
field_file =warp_file)
aw.run()
else:
outfile_std = 'cnxn not found'
F.close()
return outfile_std
def density_map(tractogram, n_sls=None, to_vox=False, normalize=False):
"""
Create a streamline density map.
based on:
https://dipy.org/documentation/1.1.1./examples_built/streamline_formats/
Parameters
----------
tractogram : StatefulTractogram
Stateful tractogram whose streamlines are used to make
the density map.
n_sls : int or None, optional
n_sls to randomly select to make the density map.
If None, all streamlines are used.
Default: None
to_vox : bool, optional
Whether to put the stateful tractogram in VOX space before making
the density map.
Default: False
normalize : bool, optional
Whether to normalize maximum values to 1.
Default: False
Returns
-------
Nifti1Image containing the density map.
"""
if to_vox:
tractogram.to_vox()
sls = tractogram.streamlines
if n_sls is not None:
sls = select_random_set_of_streamlines(sls, n_sls)
affine, vol_dims, voxel_sizes, voxel_order = get_reference_info(tractogram)
tractogram_density = dtu.density_map(sls, np.eye(4), vol_dims)
if normalize:
tractogram_density = tractogram_density / tractogram_density.max()
nifti_header = create_nifti_header(affine, vol_dims, voxel_sizes)
density_map_img = nib.Nifti1Image(tractogram_density, affine, nifti_header)
return density_map_img
def computeStats(subjectID,reference,streamlines,classification,outdir):
avg_length = []
avg_curv = []
stream_count = []
mean_x = []
mean_y = []
mean_z = []
num_vox = []
tract_index_labels = np.trim_zeros(np.unique(classification['index'].tolist()))
qb = QuickBundles(np.inf)
for i in tract_index_labels:
indices = [ t for t in range(len(classification['index'].tolist())) if classification['index'].tolist()[int(t)] == i ]
avg_length.append(np.mean(length(streamlines[indices])))
clusters = qb.cluster(streamlines[indices])
avg_curv.append(mean_curvature(clusters.centroids[0]))
orientation = mean_orientation(clusters.centroids[0])
mean_x.append(orientation[0])
mean_y.append(orientation[1])
mean_z.append(orientation[2])
stream_count.append(len(indices))
denmap = density_map(streamlines[indices],reference.affine,reference.shape)
num_vox.append(len(denmap[denmap>0]))
df = pd.DataFrame([],dtype=object)
df['subjectID'] = [ subjectID for f in range(len(classification['names'].tolist())) ]
df['structureID'] = [ f for f in classification['names'].tolist() ]
df['nodeID'] = [ 1 for f in range(len(df['structureID'])) ]
df['streamline_count'] = stream_count
df['average_length'] = avg_length
df['average_curvature'] = avg_curv
df['voxel_count'] = num_vox
df['centroid_x'] = mean_x
df['centroid_y'] = mean_y
df['centroid_z'] = mean_z
df.to_csv('%s/output_FiberStats.csv' %outdir,index=False)
def _run_interface(self, runtime):
from numpy import min_scalar_type
tracks, header = nbt.read(self.inputs.in_file)
streams = ((ii[0]) for ii in tracks)
if isdefined(self.inputs.reference):
refnii = nb.load(self.inputs.reference)
affine = refnii.get_affine()
data_dims = refnii.get_shape()[:3]
kwargs = dict(affine=affine)
else:
iflogger.warn(('voxel_dims and data_dims are deprecated'
'as of dipy 0.7.1. Please use reference '
'input instead'))
if not isdefined(self.inputs.data_dims):
data_dims = header['dim']
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header['voxel_size']
else:
voxel_size = self.inputs.voxel_dims
affine = header['vox_to_ras']
kwargs = dict(voxel_size=voxel_size)
data = density_map(streams, data_dims, **kwargs)
data = data.astype(min_scalar_type(data.max()))
img = nb.Nifti1Image(data, affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
iflogger.info(
('Track density map saved as {i}, size={d}, '
'dimensions={v}').format(
i=out_file,
d=img.get_shape(),
v=img.get_header().get_zooms()))
return runtime
def _run_interface(self, runtime):
from numpy import min_scalar_type
from dipy.tracking.utils import density_map
tracks, header = nbt.read(self.inputs.in_file)
streams = ((ii[0]) for ii in tracks)
if isdefined(self.inputs.reference):
refnii = nb.load(self.inputs.reference)
affine = refnii.affine
data_dims = refnii.shape[:3]
kwargs = dict(affine=affine)
else:
IFLOGGER.warn(
'voxel_dims and data_dims are deprecated as of dipy 0.7.1. Please use reference '
'input instead')
if not isdefined(self.inputs.data_dims):
data_dims = header['dim']
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header['voxel_size']
else:
voxel_size = self.inputs.voxel_dims
affine = header['vox_to_ras']
kwargs = dict(voxel_size=voxel_size)
data = density_map(streams, data_dims, **kwargs)
data = data.astype(min_scalar_type(data.max()))
img = nb.Nifti1Image(data, affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
IFLOGGER.info(
'Track density map saved as %s, size=%s, dimensions=%s',
out_file, img.shape, img.header.get_zooms())
return runtime
def fib_density_map(volume, fiber):
"""
fiber to volume density map
Parameters
----------
volume : volume data
fiber : streamlines data
Return
------
streamline density in each voxel
"""
shape = volume.shape
affine = volume.affine
streamlines = fiber
voxel_size = nib.affine.voxel_size(affine)
image_volume = ditu.density_map(streamlines,
vol_dims=shape,
voxel_size=voxel_size,
affine=affine)
return image_volume
def _run_interface(self, runtime):
from numpy import min_scalar_type
tracks, header = nbt.read(self.inputs.in_file)
streams = ((ii[0]) for ii in tracks)
if isdefined(self.inputs.reference):
refnii = nb.load(self.inputs.reference)
affine = refnii.get_affine()
data_dims = refnii.get_shape()[:3]
kwargs = dict(affine=affine)
else:
iflogger.warn(('voxel_dims and data_dims are deprecated'
'as of dipy 0.7.1. Please use reference '
'input instead'))
if not isdefined(self.inputs.data_dims):
data_dims = header['dim']
else:
data_dims = self.inputs.data_dims
if not isdefined(self.inputs.voxel_dims):
voxel_size = header['voxel_size']
else:
voxel_size = self.inputs.voxel_dims
affine = header['vox_to_ras']
kwargs = dict(voxel_size=voxel_size)
data = density_map(streams, data_dims, **kwargs)
data = data.astype(min_scalar_type(data.max()))
img = nb.Nifti1Image(data, affine)
out_file = op.abspath(self.inputs.out_filename)
nb.save(img, out_file)
iflogger.info(
('Track density map saved as {i}, size={d}, '
'dimensions={v}').format(i=out_file,
d=img.get_shape(),
v=img.get_header().get_zooms()))
return runtime
def filter_streamlines(dwi_file, dir_path, gtab, streamlines, life_run, min_length, conn_model, target_samples,
node_size, curv_thr_list, step_list):
from dipy.tracking import utils
from pynets.dmri.track import save_streams, run_LIFE_all
dwi_img = nib.load(dwi_file)
data = dwi_img.get_fdata()
# Flatten streamlines list, and apply min length filter
print('Filtering streamlines...')
streamlines = nib.streamlines.array_sequence.ArraySequence([s for s in streamlines if len(s) > float(min_length)])
# Fit LiFE model
if life_run is True:
print('Fitting LiFE...')
# Fit Linear Fascicle Evaluation (LiFE)
[streamlines, rmse] = run_LIFE_all(data, gtab, streamlines)
mean_rmse = np.mean(rmse)
print("%s%s" % ('Mean RMSE: ', mean_rmse))
if mean_rmse > 5:
print('WARNING: LiFE revealed high model error. Check streamlines output and review tracking parameters used.')
# Create density map
dm = utils.density_map(streamlines, dwi_img.shape, affine=np.eye(4))
# Save density map
dm_img = nib.Nifti1Image(dm.astype('int16'), dwi_img.affine)
dm_img.to_filename("%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/density_map_', conn_model, '_', target_samples, '_',
node_size, 'mm_curv', str(curv_thr_list).replace(', ', '_'),
'_step', str(step_list).replace(', ', '_'), '.nii.gz'))
# Save streamlines to trk
streams = "%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/streamlines_', conn_model, '_', target_samples, '_',
node_size, 'mm_curv', str(curv_thr_list).replace(', ', '_'),
'_step', str(step_list).replace(', ', '_'), '.trk')
streams = save_streams(dwi_img, streamlines, streams)
return streams, dir_path
def direct_streamline_norm(streams,
fa_path,
ap_path,
dir_path,
track_type,
conn_model,
subnet,
node_radius,
dens_thresh,
ID,
roi,
min_span_tree,
disp_filt,
parc,
prune,
atlas,
labels_im_file,
parcellation,
labels,
coords,
norm,
binary,
atlas_t1w,
basedir_path,
curv_thr_list,
step_list,
traversal,
min_length,
t1w_brain,
run_dsn=False):
"""
A Function to perform normalization of streamlines tracked in native
diffusion space to an MNI-space template.
Parameters
----------
streams : str
File path to save streamline array sequence in .trk format.
fa_path : str
File path to FA Nifti1Image.
ap_path : str
File path to the anisotropic power Nifti1Image.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
subnet : str
Resting-state subnet based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_radius : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone subnet' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
labels_im_file : str
File path to atlas parcellation Nifti1Image aligned to dwi space.
parcellation : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_t1w : str
File path to atlas parcellation Nifti1Image in T1w-conformed space.
basedir_path : str
Path to directory to output direct-streamline normalized temp files
and outputs.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
traversal : str
The statistical approach to tracking. Options are: det (deterministic),
closest (clos), boot (bootstrapped), and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
t1w_brain : str
File path to the T1w Nifti1Image.
Returns
-------
streams_warp : str
File path to normalized streamline array sequence in .trk format.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
subnet : str
Resting-state subnet based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_radius : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone subnet' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
parcellation : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_for_streams : str
File path to atlas parcellation Nifti1Image in the same
morphological space as the streamlines.
traversal : str
The statistical approach to tracking. Options are: det
(deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
warped_fa : str
File path to MNI-space warped FA Nifti1Image.
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
References
----------
.. [1] Greene, C., Cieslak, M., & Grafton, S. T. (2017). Effect of
different spatial normalization approaches on tractography and structural
brain subnets. subnet Neuroscience, 1-19.
"""
import gc
from dipy.tracking.streamline import transform_streamlines
from pynets.registration import utils as regutils
from pynets.plotting.brain import show_template_bundles
import os.path as op
from dipy.io.streamline import load_tractogram
from dipy.tracking._utils import _mapping_to_voxel
from dipy.tracking.utils import density_map
from dipy.io.stateful_tractogram import Space, StatefulTractogram, Origin
from dipy.io.streamline import save_tractogram
if run_dsn is True:
dsn_dir = f"{basedir_path}/dmri_reg/DSN"
if not op.isdir(dsn_dir):
os.mkdir(dsn_dir)
namer_dir = f"{dir_path}/tractography"
if not op.isdir(namer_dir):
os.mkdir(namer_dir)
atlas_img = nib.load(labels_im_file)
# Run SyN and normalize streamlines
fa_img = nib.load(fa_path)
atlas_t1w_img = nib.load(atlas_t1w)
t1w_brain_img = nib.load(t1w_brain)
brain_mask = np.asarray(t1w_brain_img.dataobj).astype("bool")
streams_t1w = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/streamlines_t1w_",
"%s" % (subnet + "_" if subnet is not None else ""),
"%s" %
(op.basename(roi).split(".")[0] + "_" if roi is not None else ""),
conn_model,
"%s" % ("%s%s" % ("_" + str(node_radius), "mm_") if
((node_radius != "parc") and
(node_radius is not None)) else "_"),
"curv",
str(curv_thr_list).replace(", ", "_"),
"step",
str(step_list).replace(", ", "_"),
"tracktype-",
track_type,
"_traversal-",
traversal,
"_minlength-",
min_length,
".trk",
)
density_t1w = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/density_map_t1w_",
"%s" % (subnet + "_" if subnet is not None else ""),
"%s" %
(op.basename(roi).split(".")[0] + "_" if roi is not None else ""),
conn_model,
"%s" % ("%s%s" % ("_" + str(node_radius), "mm_") if
((node_radius != "parc") and
(node_radius is not None)) else "_"),
"curv",
str(curv_thr_list).replace(", ", "_"),
"step",
str(step_list).replace(", ", "_"),
"tracktype-",
track_type,
"_traversal-",
traversal,
"_minlength-",
min_length,
".nii.gz",
)
streams_warp_png = '/tmp/dsn.png'
# SyN FA->Template
[mapping, affine_map,
warped_fa] = regutils.wm_syn(t1w_brain, ap_path, dsn_dir)
tractogram = load_tractogram(
streams,
fa_img,
to_origin=Origin.NIFTI,
to_space=Space.VOXMM,
bbox_valid_check=False,
)
fa_img.uncache()
streamlines = tractogram.streamlines
warped_fa_img = nib.load(warped_fa)
warped_fa_affine = warped_fa_img.affine
warped_fa_shape = warped_fa_img.shape
streams_in_curr_grid = transform_streamlines(streamlines,
affine_map.affine_inv)
streams_final_filt = regutils.warp_streamlines(t1w_brain_img.affine,
fa_img.affine, mapping,
warped_fa_img,
streams_in_curr_grid,
brain_mask)
# Remove streamlines with negative voxel indices
lin_T, offset = _mapping_to_voxel(np.eye(4))
streams_final_filt_final = []
for sl in streams_final_filt:
inds = np.dot(sl, lin_T)
inds += offset
if not inds.min().round(decimals=6) < 0:
streams_final_filt_final.append(sl)
# Save streamlines
stf = StatefulTractogram(
streams_final_filt_final,
reference=t1w_brain_img,
space=Space.VOXMM,
origin=Origin.NIFTI,
)
stf.remove_invalid_streamlines()
streams_final_filt_final = stf.streamlines
save_tractogram(stf, streams_t1w, bbox_valid_check=True)
warped_fa_img.uncache()
# DSN QC plotting
show_template_bundles(streams_final_filt_final, atlas_t1w,
streams_warp_png)
nib.save(
nib.Nifti1Image(
density_map(streams_final_filt_final,
affine=np.eye(4),
vol_dims=warped_fa_shape),
warped_fa_affine,
),
density_t1w,
)
del (
tractogram,
streamlines,
stf,
streams_final_filt_final,
streams_final_filt,
streams_in_curr_grid,
brain_mask,
)
gc.collect()
assert len(coords) == len(labels)
atlas_for_streams = atlas_t1w
else:
print(
"Skipping Direct Streamline Normalization (DSN). Will proceed to "
"define fiber connectivity in native diffusion space...")
streams_t1w = streams
warped_fa = fa_path
atlas_for_streams = labels_im_file
return (streams_t1w, dir_path, track_type, conn_model, subnet, node_radius,
dens_thresh, ID, roi, min_span_tree, disp_filt, parc, prune, atlas,
parcellation, labels, coords, norm, binary, atlas_for_streams,
traversal, warped_fa, min_length)
However, the interpretation of streamline counts can be tricky. The relationship between the underlying biology and the streamline counts will depend on several factors, including how the tracking was done, and the correct way to interpret these kinds of connectivity matrices is still an open question in the diffusion imaging literature. The next function we'll demonstrate is ``density_map``. This function allows one to represent the spatial distribution of a track by counting the density of streamlines in each voxel. For example, let's take the track connecting the left and right superior frontal gyrus. """ lr_superiorfrontal_track = grouping[11, 54] shape = labels.shape dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine) """ Let's save this density map and the streamlines so that they can be visualized together. In order to save the streamlines in a ".trk" file we'll need to move them to "trackvis space", or the representation of streamlines specified by the trackvis Track File format. To do that, we will use tools available in `nibabel <http://nipy.org/nibabel>`_) """ import nibabel as nib from dipy.io.stateful_tractogram import Space, StatefulTractogram from dipy.io.streamline import save_trk # Save density map
However, the interpretation of streamline counts can be tricky. The
relationship between the underlying biology and the streamline counts will
depend on several factors, including how the tracking was done, and the correct
way to interpret these kinds of connectivity matrices is still an open question
in the diffusion imaging literature.
The next function we'll demonstrate is ``density_map``. This function allows
one to represent the spatial distribution of a track by counting the density of
streamlines in each voxel. For example, let's take the track connecting the
left and right superior frontal gyrus.
"""
lr_superiorfrontal_track = grouping[11, 54]
shape = labels.shape
dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
"""
Let's save this density map and the streamlines so that they can be
visualized together. In order to save the streamlines in a ".trk" file we'll
need to move them to "trackvis space", or the representation of streamlines
specified by the trackvis Track File format.
To do that, we will use tools available in `nibabel <http://nipy.org/nibabel>`_)
"""
import nibabel as nib
from dipy.io.streamline import save_trk
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.affine)
dm_img.to_filename("lr-superiorfrontal-dm.nii.gz")
However, the interpretation of streamline counts can be tricky. The
relationship between the underlying biology and the streamline counts will
depend on several factors, including how the tracking was done, and the correct
way to interpret these kinds of connectivity matrices is still an open question
in the diffusion imaging literature.
The next function we'll demonstrate is ``density_map``. This function allows
one to represent the spatial distribution of a track by counting the density of
streamlines in each voxel. For example, let's take the track connecting the
left and right superior frontal gyrus.
"""
lr_superiorfrontal_track = grouping[11, 54]
shape = labels.shape
dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
"""
Let's save this density map and the streamlines so that they can be
visualized together. In order to save the streamlines in a ".trk" file we'll
need to move them to "trackvis space", or the representation of streamlines
specified by the trackvis Track File format.
To do that, we will use tools available in `nibabel <http://nipy.org/nibabel>`_)
"""
import nibabel as nib
from dipy.io.streamline import save_trk
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.affine)
import pandas as pd
import dipy.tracking.utils as ut
import random
#you'll need to be in the right directory for this to work
import WMA_pyFuncs
# esablish path to tractogram
pathToTestTractogram = '/media/dan/storage/data/exploreTractography/test_1M_1.tck'
testTractogram = nib.streamlines.load(pathToTestTractogram)
# establish path to reference T1
pathToTestT1 = '/media/dan/storage/data/exploreTractography/T1_in_b0_unifized_GM.nii.gz'
testT1 = nib.load(pathToTestT1)
# get a mask of the whole brain tract
tractMask = ut.density_map(testTractogram.streamlines, testT1.affine,
testT1.shape)
#extract these as coordinates
imgSpaceTractVoxels = np.array(np.where(tractMask)).T
subjectSpaceTractCoords = nib.affines.apply_affine(testT1.affine,
imgSpaceTractVoxels)
# create a dataframe for the output
outputDataframe = pd.DataFrame(columns=[
'dipyTime', 'modifiedTime', 'dipyCount', 'modifiedCount', 'testRadius',
'operation'
])
#arbitrarily set the number of rois per test.
#Increasing this beyond 2 isn't that helpful because its highly unlikely that
#any given streamline passes though 3 randomly placed spheres
roiNumToTest = 2
def fiber_density_map(tracks,
template,
outdir,
basename=None,
fiber_ends_only=False,
overlay=False,
overlay_alpha=None,
ext=".png",
axes="RAS"):
""" Create a density map of fibers or fiber-ends as a Nifti, along with a
snap file if requested.
Parameters
----------
tracks: str
Path to the streamlines. It has to be loadable by nibabel.
template: str
Path to Nifti defining the space of output density map (e.g. nodif).
outdir: str
Path to directory where to output.
basename: str, default None
Filename without extension of output files. By default None, means
"fiber_ends_density" if 'fiber_ends_only' is True else "fiber_density".
fiber_ends_only: bool, default False
If true, create a density map of fiber ends.
overlay: bool, default False
If True, fibers are overlayed on the template in the PDF snapshot.
Otherwise the fiber density is plotted alone.
overlay_alpha: float, default None
fix the overlay alpha value (0-1).
ext: str, default '.png'
Snapshot extension, used to specify the output format.
axes: str, default 'RAS'
orientation of the original axes X, Y, and Z
specified with the following convention: L=Left, R=Right,
A=Anterion, P=Posterior, I=Inferior, S=Superior.
Return
------
fiber_density_map: str
Path to the output Nifti density map.
fiber_density_snap: str or None
Path to the output PDF file, if requested (create_pdf = True).
"""
# Import here to avoid dependency on dipy & pydcmio for the whole package
from dipy.tracking.utils import density_map
from pydcmio.plotting.slicer import mosaic
# Create the default output file name
if basename is None:
basename = "fiber_ends_density" if fiber_ends_only else "fiber_density"
# Load tracks
tracks = nibabel.streamlines.load(tracks)
# Load template image: needed to get affine matrix and shape
template_im = nibabel.load(template)
# If user has requested density of fiber ends: keep only the end points
# of the fibers as a 2d-array
if fiber_ends_only:
streamlines = [arr[[0, -1]] for arr in tracks.streamlines]
else:
streamlines = tracks.streamlines
# Compute fiber count in each voxel
density_map_arr = density_map(streamlines=streamlines,
vol_dims=template_im.get_shape(),
affine=tracks.affine)
# Create and write Nifti
fiber_density_map = os.path.join(outdir, basename + ".nii.gz")
density_map_nii = nibabel.Nifti1Image(density_map_arr.astype("int"),
template_im.get_affine())
density_map_nii.to_filename(fiber_density_map)
# Swap axes
if axes != "RAS":
reorient_image(in_file=fiber_density_map,
axes=axes,
prefix="",
output_directory=None,
is_direct=False)
# If requested use fibers as overlay on the template
kwargs = dict(outdir=outdir,
title="Density Map",
basename=basename,
ext=ext,
overlay_alpha=overlay_alpha)
if overlay:
fiber_density_snap = mosaic(impath=template,
overlay=fiber_density_map,
**kwargs)
else:
fiber_density_snap = mosaic(impath=fiber_density_map, **kwargs)
return fiber_density_map, fiber_density_snap
def create_density_map(
fa_img,
dir_path,
streamlines,
conn_model,
node_radius,
curv_thr_list,
step_list,
subnet,
roi,
traversal,
min_length,
namer_dir,
):
"""
Create a density map of the list of streamlines.
Parameters
----------
fa_img : Nifti1Image
Dwi data stored as a Nifti1image object.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
node_radius : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
subnet : str
Resting-state subnet based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
traversal : str
The statistical approach to tracking. Options are: det (deterministic),
closest (clos), boot (bootstrapped), and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
Returns
-------
streams : str
File path to saved streamline array sequence in DTK-compatible
trackvis (.trk) format.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
dm_path : str
File path to fiber density map Nifti1Image.
"""
import os.path as op
from dipy.tracking import utils
from dipy.tracking._utils import _mapping_to_voxel
# Remove streamlines with negative voxel indices
lin_T, offset = _mapping_to_voxel(np.eye(4))
streams_filt = []
for sl in streamlines:
inds = np.dot(sl, lin_T)
inds += offset
if not inds.min().round(decimals=6) < 0:
streams_filt.append(sl)
# Create density map
dm = utils.density_map(streams_filt,
affine=np.eye(4),
vol_dims=fa_img.shape)
# Save density map
dm_img = nib.Nifti1Image(dm.astype("float32"), fa_img.affine)
dm_path = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/density_map_",
"%s" % (subnet + "_" if subnet is not None else ""),
"%s" %
(op.basename(roi).split(".")[0] + "_" if roi is not None else ""),
conn_model,
"_",
"%s" % ("%s%s" % (node_radius, "mm_") if
((node_radius != "parc") and
(node_radius is not None)) else "parc_"),
"curv-",
str(curv_thr_list).replace(", ", "_"),
"_step-",
str(step_list).replace(", ", "_"),
"_traversal-",
traversal,
"_minlength-",
min_length,
".nii.gz",
)
dm_img.to_filename(dm_path)
del streamlines, streams_filt
dm_img.uncache()
return dir_path, dm_path
However, the interpretation of streamline counts can be tricky. The
relationship between the underlying biology and the streamline counts will
depend on several factors, including how the tracking was done, and the correct
way to interpret these kinds of connectivity matrices is still an open question
in the diffusion imaging literature.
The next function we'll demonstrate is ``density_map``. This function allows
one to represent the spatial distribution of a track by counting the density of
streamlines in each voxel. For example, let's take the track connecting the
left and right superior frontal gyrus.
"""
lr_superiorfrontal_track = grouping[11, 54]
shape = labels.shape
dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
"""
Let's save this density map and the streamlines so that they can be
visualized together. In order to save the streamlines in a ".trk" file we'll
need to move them to "trackvis space", or the representation of streamlines
specified by the trackvis Track File format.
To do that, we will use tools available in [nibabel](http://nipy.org/nibabel)
"""
import nibabel as nib
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.get_affine())
dm_img.to_filename("lr-superiorfrontal-dm.nii.gz")
def save_results(table, area_pairs, medium, subject_name):
with open('saved/{}_track_result'.format(subject_name), 'wb') as f:
pickle.dump(table, f)
streamlines = table['streamlines']
sft = table['sft']
sub_affine = table['affine']
data = table['data']
labels = table["labels"]
affine = np.eye(4)
# extract streamlines only pass through ROIs
cc_slice = (labels == medium[0]) # ROIs
if len(medium) > 1:
for m in medium:
cc_slice = cc_slice | (labels == m)
cc_streamlines = utils.target(streamlines, affine, cc_slice)
cc_streamlines = Streamlines(cc_streamlines)
other_streamlines = utils.target(streamlines,
affine,
cc_slice,
include=False)
other_streamlines = Streamlines(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
M, grouping = utils.connectivity_matrix(streamlines,
affine,
labels,
return_mapping=True,
mapping_as_streamlines=True)
M[:3, :] = 0
M[:, :3] = 0
for pair in area_pairs:
track = grouping[pair[0], pair[1]]
shape = labels.shape
dm = utils.density_map(track, affine, shape)
import nibabel as nib
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), sub_affine)
dm_img.to_filename("saved/{}_{}.nii.gz".format(subject_name, pair))
# save_trk(sft, "tractogram_probabilistic_dg.trk")
# # visualzie
# # Enables/disables interactive visualization
# interactive = False
# if has_fury:
# # Prepare the display objects.
# color = colormap.line_colors(streamlines)
# streamlines_actor = actor.line(streamlines, color)
# # Create the 3D display.
# r = window.Scene()
# r.add(streamlines_actor)
# # Save still images for this static example. Or for interactivity use
# window.record(r, out_path='fiber_tracking_Caudate_l_r_result.png', size=(800, 800))
# if interactive:
# window.show(r)
def label_streamlines_density(streamlines, labels, affine, f_name, img,
label_img):
"""
.. figure:: connectivity.png
:align: center
**Connectivity of Corpus Callosum**
.. include:: ../links_names.inc
"""
shape = labels.shape
dm = utils.density_map(streamlines, shape, affine=affine)
sum = 0
count = 0
for i in dm:
for j in i:
for k in j:
if (k != 0):
sum = sum + k
count += 1
density = sum * 1.0 / count
print density
"""
To do that, we will use tools available in [nibabel](http://nipy.org/nibabel)
"""
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), img.get_affine())
dm_img.to_filename(f_name + "-dm.nii.gz")
# Make a trackvis header so we can save streamlines
voxel_size = label_img.get_header().get_zooms()
trackvis_header = nib.trackvis.empty_header()
trackvis_header['voxel_size'] = voxel_size
trackvis_header['dim'] = shape
trackvis_header['voxel_order'] = "RAS"
# Move streamlines to "trackvis space"
trackvis_point_space = utils.affine_for_trackvis(voxel_size)
lr_sf_trk = utils.move_streamlines(streamlines,
trackvis_point_space,
input_space=affine)
lr_sf_trk = list(lr_sf_trk)
"""
# Save streamlines
for_save = [(sl, None, None) for sl in lr_sf_trk]
nib.trackvis.write(f_name+"_label1.trk", for_save, trackvis_header)
"""
"""
import tractconverter as tc
density_file = f_name+"_label1.trk"
input_format=tc.detect_format(density_file)
input=input_format(density_file)
output=tc.FORMATS['vtk'].create(density_file+".vtk",input.hdr)
tc.convert(input,output)
"""
"""
Let's take a moment here to consider the representation of streamlines used in
dipy. Streamlines are a path though the 3d space of an image represented by a
set of points. For these points to have a meaningful interpretation, these
points must be given in a known coordinate system. The ``affine`` attribute of
the ``streamline_generator`` object specifies the coordinate system of the
points with respect to the voxel indices of the input data.
``trackvis_point_space`` specifies the trackvis coordinate system with respect
to the same indices. The ``move_streamlines`` function returns a new set of
streamlines from an existing set of streamlines in the target space. The
target space and the input space must be specified as affine transformations
with respect to the same reference [#]_. If no input space is given, the input
space will be the same as the current representation of the streamlines, in
other words the input space is assumed to be ``np.eye(4)``, the 4-by-4 identity
matrix.
All of the functions above that allow streamlines to interact with volumes take
an affine argument. This argument allows these functions to work with
streamlines regardless of their coordinate system. For example even though we
moved our streamlines to "trackvis space", we can still compute the density map
as long as we specify the right coordinate system.
"""
dm_trackvis = utils.density_map(lr_sf_trk,
shape,
affine=trackvis_point_space)
assert np.all(dm == dm_trackvis)
return dm, density
"""
This means that streamlines can interact with any image volume, for example a
high resolution structural image, as long as one can register that image to
the diffusion images and calculate the coordinate system with respect to that
image.
"""
"""
lpt_sft.to_corner()
rpt_sft.to_corner()
cc_streamlines_vox = select_random_set_of_streamlines(cc_sft.streamlines, 1000)
laf_streamlines_vox = select_random_set_of_streamlines(laf_sft.streamlines,
1000)
raf_streamlines_vox = select_random_set_of_streamlines(raf_sft.streamlines,
1000)
lpt_streamlines_vox = select_random_set_of_streamlines(lpt_sft.streamlines,
1000)
rpt_streamlines_vox = select_random_set_of_streamlines(rpt_sft.streamlines,
1000)
# Same dimensions for every stateful tractogram, can be re-use
affine, dimensions, voxel_sizes, voxel_order = cc_sft.space_attributes
cc_density = density_map(cc_streamlines_vox, np.eye(4), dimensions)
laf_density = density_map(laf_streamlines_vox, np.eye(4), dimensions)
raf_density = density_map(raf_streamlines_vox, np.eye(4), dimensions)
lpt_density = density_map(lpt_streamlines_vox, np.eye(4), dimensions)
rpt_density = density_map(rpt_streamlines_vox, np.eye(4), dimensions)
"""
Replacing streamlines is possible, but if the state was modified between
operations such as this one is not recommended:
-> cc_sft.streamlines = cc_streamlines_vox
It is recommended to re-create a new StatefulTractogram object and
explicitly specify in which space the streamlines are. Be careful to follow
the order of operations.
If the tractogram was from a Trk file with metadata, this will be lost.
If you wish to keep metadata while manipulating the number or the order
def label_streamlines_density(streamlines,labels,affine,f_name,img,label_img):
"""
.. figure:: connectivity.png
:align: center
**Connectivity of Corpus Callosum**
.. include:: ../links_names.inc
"""
shape = labels.shape
dm = utils.density_map(streamlines, shape, affine=affine)
sum=0 ;count=0
for i in dm:
for j in i:
for k in j:
if (k != 0):
sum=sum+k
count += 1
density = sum*1.0/count
print density
"""
To do that, we will use tools available in [nibabel](http://nipy.org/nibabel)
"""
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), img.get_affine())
dm_img.to_filename(f_name+"-dm.nii.gz")
# Make a trackvis header so we can save streamlines
voxel_size = label_img.get_header().get_zooms()
trackvis_header = nib.trackvis.empty_header()
trackvis_header['voxel_size'] = voxel_size
trackvis_header['dim'] = shape
trackvis_header['voxel_order'] = "RAS"
# Move streamlines to "trackvis space"
trackvis_point_space = utils.affine_for_trackvis(voxel_size)
lr_sf_trk = utils.move_streamlines(streamlines,
trackvis_point_space, input_space=affine)
lr_sf_trk = list(lr_sf_trk)
"""
# Save streamlines
for_save = [(sl, None, None) for sl in lr_sf_trk]
nib.trackvis.write(f_name+"_label1.trk", for_save, trackvis_header)
"""
"""
import tractconverter as tc
density_file = f_name+"_label1.trk"
input_format=tc.detect_format(density_file)
input=input_format(density_file)
output=tc.FORMATS['vtk'].create(density_file+".vtk",input.hdr)
tc.convert(input,output)
"""
"""
Let's take a moment here to consider the representation of streamlines used in
dipy. Streamlines are a path though the 3d space of an image represented by a
set of points. For these points to have a meaningful interpretation, these
points must be given in a known coordinate system. The ``affine`` attribute of
the ``streamline_generator`` object specifies the coordinate system of the
points with respect to the voxel indices of the input data.
``trackvis_point_space`` specifies the trackvis coordinate system with respect
to the same indices. The ``move_streamlines`` function returns a new set of
streamlines from an existing set of streamlines in the target space. The
target space and the input space must be specified as affine transformations
with respect to the same reference [#]_. If no input space is given, the input
space will be the same as the current representation of the streamlines, in
other words the input space is assumed to be ``np.eye(4)``, the 4-by-4 identity
matrix.
All of the functions above that allow streamlines to interact with volumes take
an affine argument. This argument allows these functions to work with
streamlines regardless of their coordinate system. For example even though we
moved our streamlines to "trackvis space", we can still compute the density map
as long as we specify the right coordinate system.
"""
dm_trackvis = utils.density_map(lr_sf_trk, shape, affine=trackvis_point_space)
assert np.all(dm == dm_trackvis)
return dm,density
"""
This means that streamlines can interact with any image volume, for example a
high resolution structural image, as long as one can register that image to
the diffusion images and calculate the coordinate system with respect to that
image.
"""
"""
def direct_streamline_norm(
streams,
fa_path,
ap_path,
dir_path,
track_type,
target_samples,
conn_model,
network,
node_size,
dens_thresh,
ID,
roi,
min_span_tree,
disp_filt,
parc,
prune,
atlas,
labels_im_file,
uatlas,
labels,
coords,
norm,
binary,
atlas_mni,
basedir_path,
curv_thr_list,
step_list,
directget,
min_length,
error_margin,
t1_aligned_mni
):
"""
A Function to perform normalization of streamlines tracked in native
diffusion space to an MNI-space template.
Parameters
----------
streams : str
File path to save streamline array sequence in .trk format.
fa_path : str
File path to FA Nifti1Image.
ap_path : str
File path to the anisotropic power Nifti1Image.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
target_samples : int
Total number of streamline samples specified to generate streams.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_size : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
labels_im_file : str
File path to atlas parcellation Nifti1Image aligned to dwi space.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
basedir_path : str
Path to directory to output direct-streamline normalized temp files
and outputs.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
directget : str
The statistical approach to tracking. Options are: det (deterministic),
closest (clos), boot (bootstrapped), and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
t1_aligned_mni : str
File path to the T1w Nifti1Image in template MNI space.
Returns
-------
streams_warp : str
File path to normalized streamline array sequence in .trk format.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
target_samples : int
Total number of streamline samples specified to generate streams.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
node_size : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to graph nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
directget : str
The statistical approach to tracking. Options are: det
(deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
warped_fa : str
File path to MNI-space warped FA Nifti1Image.
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
References
----------
.. [1] Greene, C., Cieslak, M., & Grafton, S. T. (2017). Effect of
different spatial normalization approaches on tractography and structural
brain networks. Network Neuroscience, 1-19.
"""
import sys
import gc
from dipy.tracking.streamline import transform_streamlines
from pynets.registration import reg_utils as regutils
# from pynets.plotting import plot_gen
import pkg_resources
import yaml
import os.path as op
from pynets.registration.reg_utils import vdc
from nilearn.image import resample_to_img
from dipy.io.streamline import load_tractogram
from dipy.tracking import utils
from dipy.tracking._utils import _mapping_to_voxel
from dipy.io.stateful_tractogram import Space, StatefulTractogram, Origin
from dipy.io.streamline import save_tractogram
# from pynets.core.utils import missing_elements
with open(
pkg_resources.resource_filename("pynets", "runconfig.yaml"), "r"
) as stream:
try:
hardcoded_params = yaml.load(stream)
run_dsn = hardcoded_params['tracking']["DSN"][0]
except FileNotFoundError as e:
import sys
print(e, "Failed to parse runconfig.yaml")
exit(1)
stream.close()
if run_dsn is True:
dsn_dir = f"{basedir_path}/dmri_reg/DSN"
if not op.isdir(dsn_dir):
os.mkdir(dsn_dir)
namer_dir = f"{dir_path}/tractography"
if not op.isdir(namer_dir):
os.mkdir(namer_dir)
atlas_img = nib.load(labels_im_file)
# Run SyN and normalize streamlines
fa_img = nib.load(fa_path)
vox_size = fa_img.header.get_zooms()[0]
template_path = pkg_resources.resource_filename(
"pynets", f"templates/FA_{int(vox_size)}mm.nii.gz"
)
if sys.platform.startswith('win') is False:
try:
template_img = nib.load(template_path)
except indexed_gzip.ZranError as e:
print(e,
f"\nCannot load FA template. Do you have git-lfs "
f"installed?")
sys.exit(1)
else:
try:
template_img = nib.load(template_path)
except ImportError as e:
print(e, f"\nCannot load FA template. Do you have git-lfs "
f"installed?")
sys.exit(1)
uatlas_mni_img = nib.load(atlas_mni)
t1_aligned_mni_img = nib.load(t1_aligned_mni)
brain_mask = np.asarray(t1_aligned_mni_img.dataobj).astype("bool")
streams_mni = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/streamlines_mni_",
"%s" % (network + "_" if network is not None else ""),
"%s" % (op.basename(roi).split(".")[0] + "_" if roi is not None
else ""),
conn_model,
"_",
target_samples,
"%s"
% (
"%s%s" % ("_" + str(node_size), "mm_")
if ((node_size != "parc") and (node_size is not None))
else "_"
),
"curv",
str(curv_thr_list).replace(", ", "_"),
"step",
str(step_list).replace(", ", "_"),
"tracktype-",
track_type,
"_directget-",
directget,
"_minlength-",
min_length,
"_tol-",
error_margin,
".trk",
)
density_mni = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/density_map_mni_",
"%s" % (network + "_" if network is not None else ""),
"%s" % (op.basename(roi).split(".")[0] + "_" if roi is not None
else ""),
conn_model,
"_",
target_samples,
"%s"
% (
"%s%s" % ("_" + str(node_size), "mm_")
if ((node_size != "parc") and (node_size is not None))
else "_"
),
"curv",
str(curv_thr_list).replace(", ", "_"),
"step",
str(step_list).replace(", ", "_"),
"tracktype-",
track_type,
"_directget-",
directget,
"_minlength-",
min_length,
"_tol-",
error_margin,
".nii.gz",
)
# streams_warp_png = '/tmp/dsn.png'
# SyN FA->Template
[mapping, affine_map, warped_fa] = regutils.wm_syn(
template_path, fa_path, t1_aligned_mni, ap_path, dsn_dir
)
tractogram = load_tractogram(
streams,
fa_img,
to_origin=Origin.NIFTI,
to_space=Space.VOXMM,
bbox_valid_check=False,
)
fa_img.uncache()
streamlines = tractogram.streamlines
warped_fa_img = nib.load(warped_fa)
warped_fa_affine = warped_fa_img.affine
warped_fa_shape = warped_fa_img.shape
streams_in_curr_grid = transform_streamlines(
streamlines, warped_fa_affine)
# Create isocenter mapping where we anchor the origin transformation
# affine to the corner of the FOV by scaling x, y, z offsets according
# to a multiplicative van der Corput sequence with a base value equal
# to the voxel resolution
[x_mul, y_mul, z_mul] = [vdc(i, vox_size) for i in range(1, 4)]
ref_grid_aff = vox_size * np.eye(4)
ref_grid_aff[3][3] = 1
streams_final_filt = []
i = 0
# Test for various types of voxel-grid configurations
combs = [(-x_mul, -y_mul, -z_mul), (-x_mul, -y_mul, z_mul),
(-x_mul, y_mul, -z_mul), (x_mul, -y_mul, -z_mul),
(x_mul, y_mul, z_mul)]
while len(streams_final_filt)/len(streams_in_curr_grid) < 0.90:
print(f"Warping streamlines to MNI space. Attempt {i}...")
print(len(streams_final_filt)/len(streams_in_curr_grid))
adjusted_affine = affine_map.affine.copy()
if i > len(combs) - 1:
raise ValueError('DSN failed. Header orientation '
'information may be corrupted. '
'Is your dataset oblique?')
adjusted_affine[0][3] = adjusted_affine[0][3] * combs[i][0]
adjusted_affine[1][3] = adjusted_affine[1][3] * combs[i][1]
adjusted_affine[2][3] = adjusted_affine[2][3] * combs[i][2]
streams_final_filt = regutils.warp_streamlines(adjusted_affine,
ref_grid_aff,
mapping,
warped_fa_img,
streams_in_curr_grid,
brain_mask)
i += 1
# Remove streamlines with negative voxel indices
lin_T, offset = _mapping_to_voxel(np.eye(4))
streams_final_filt_final = []
for sl in streams_final_filt:
inds = np.dot(sl, lin_T)
inds += offset
if not inds.min().round(decimals=6) < 0:
streams_final_filt_final.append(sl)
# Save streamlines
stf = StatefulTractogram(
streams_final_filt_final,
reference=uatlas_mni_img,
space=Space.VOXMM,
origin=Origin.NIFTI,
)
stf.remove_invalid_streamlines()
streams_final_filt_final = stf.streamlines
save_tractogram(stf, streams_mni, bbox_valid_check=True)
warped_fa_img.uncache()
# DSN QC plotting
# plot_gen.show_template_bundles(streams_final_filt_final, atlas_mni,
# streams_warp_png) plot_gen.show_template_bundles(streamlines,
# fa_path, streams_warp_png)
# Create and save MNI density map
nib.save(
nib.Nifti1Image(
utils.density_map(
streams_final_filt_final,
affine=np.eye(4),
vol_dims=warped_fa_shape),
warped_fa_affine,
),
density_mni,
)
# Map parcellation from native space back to MNI-space and create an
# 'uncertainty-union' parcellation with original mni-space uatlas
warped_uatlas = affine_map.transform_inverse(
mapping.transform(
np.asarray(atlas_img.dataobj).astype("int"),
interpolation="nearestneighbour",
),
interp="nearest",
)
atlas_img.uncache()
warped_uatlas_img_res_data = np.asarray(
resample_to_img(
nib.Nifti1Image(warped_uatlas, affine=warped_fa_affine),
uatlas_mni_img,
interpolation="nearest",
clip=False,
).dataobj
)
uatlas_mni_data = np.asarray(uatlas_mni_img.dataobj)
uatlas_mni_img.uncache()
overlap_mask = np.invert(
warped_uatlas_img_res_data.astype("bool") *
uatlas_mni_data.astype("bool"))
os.makedirs(f"{dir_path}/parcellations", exist_ok=True)
atlas_mni = f"{dir_path}/parcellations/" \
f"{op.basename(uatlas).split('.nii')[0]}_liberal.nii.gz"
nib.save(
nib.Nifti1Image(
warped_uatlas_img_res_data * overlap_mask.astype("int")
+ uatlas_mni_data * overlap_mask.astype("int")
+ np.invert(overlap_mask).astype("int")
* warped_uatlas_img_res_data.astype("int"),
affine=uatlas_mni_img.affine,
),
atlas_mni,
)
del (
tractogram,
streamlines,
warped_uatlas_img_res_data,
uatlas_mni_data,
overlap_mask,
stf,
streams_final_filt_final,
streams_final_filt,
streams_in_curr_grid,
brain_mask,
)
gc.collect()
assert len(coords) == len(labels)
else:
print(
"Skipping Direct Streamline Normalization (DSN). Will proceed to "
"define fiber connectivity in native diffusion space...")
streams_mni = streams
warped_fa = fa_path
atlas_mni = labels_im_file
return (
streams_mni,
dir_path,
track_type,
target_samples,
conn_model,
network,
node_size,
dens_thresh,
ID,
roi,
min_span_tree,
disp_filt,
parc,
prune,
atlas,
uatlas,
labels,
coords,
norm,
binary,
atlas_mni,
directget,
warped_fa,
min_length,
error_margin
)
However, the interpretation of streamline counts can be tricky. The
relationship between the underlying biology and the streamline counts will
depend on several factors, including how the tracking was done, and the correct
way to interpret these kinds of connectivity matrices is still an open question
in the diffusion imaging literature.
The next function we'll demonstrate is ``density_map``. This function allows
one to represent the spatial distribution of a track by counting the density of
streamlines in each voxel. For example, let's take the track connecting the
left and right superior frontal gyrus.
"""
lr_superiorfrontal_track = grouping[11, 54]
shape = labels.shape
dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
"""
Let's save this density map and the streamlines so that they can be
visualized together. In order to save the streamlines in a ".trk" file we'll
need to move them to "trackvis space", or the representation of streamlines
specified by the trackvis Track File format.
To do that, we will use tools available in [nibabel](http://nipy.org/nibabel)
"""
import nibabel as nib
# Save density map
dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.affine)
dm_img.to_filename("lr-superiorfrontal-dm.nii.gz")
streamlines = [s[0] for s in streams] # list of 2d ndarrays
if tracking_format == "trk_legacy":
streams, hdr = trackvis.read(file_in)
streamlines = [s[0] for s in streams]
else:
sl_file = nib.streamlines.load(file_in)
streamlines = sl_file.streamlines
#Upsample Streamlines (very important, especially when using DensityMap Threshold. Without upsampling eroded results)
max_seq_len = abs(ref_affine[0, 0] / 4)
streamlines = list(utils_trk.subsegment(streamlines, max_seq_len))
# Remember: Does not count if a fibers has no node inside of a voxel -> upsampling helps, but not perfect
# Counts the number of unique streamlines that pass through each voxel -> oversampling does not distort result
dm = utils_trk.density_map(streamlines, ref_shape, affine=ref_affine)
# Create Binary Map
dm_binary = dm > 0 # Using higher Threshold problematic, because tends to remove valid parts (sparse fibers)
dm_binary_c = dm_binary
#Filter Blobs (might remove valid parts) -> do not use
#dm_binary_c = remove_small_blobs(dm_binary_c, threshold=10)
#Closing of Holes (not ideal because tends to remove valid holes, e.g. in MCP) -> do not use
# size = 1
# dm_binary_c = ndimage.binary_closing(dm_binary_c, structure=np.ones((size, size, size))).astype(dm_binary.dtype)
#Save Binary Mask
dm_binary_img = nib.Nifti1Image(dm_binary_c.astype("uint8"), ref_affine)
nib.save(dm_binary_img, file_out)
def run(context):
####################################################
# Get the path to input files and other parameter #
####################################################
analysis_data = context.fetch_analysis_data()
settings = analysis_data['settings']
postprocessing = settings['postprocessing']
dataset = settings['dataset']
if dataset == "HCPL":
dwi_file_handle = context.get_files('input', modality='HARDI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.hcpl.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.hcpl.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
elif dataset == "DSI":
dwi_file_handle = context.get_files('input', modality='DSI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
else:
context.set_progress(message='Wrong dataset parameter')
inject_file_handle = context.get_files(
'input', reg_expression='.*prep.inject.nii.gz')[0]
inject_file_path = inject_file_handle.download('/root/')
VUMC_ROIs_file_handle = context.get_files(
'input', reg_expression='.*VUMC_ROIs.nii.gz')[0]
VUMC_ROIs_file_path = VUMC_ROIs_file_handle.download('/root/')
###############################
# _____ _____ _______ __ #
# | __ \_ _| __ \ \ / / #
# | | | || | | |__) \ \_/ / #
# | | | || | | ___/ \ / #
# | |__| || |_| | | | #
# |_____/_____|_| |_| #
# #
###############################
########################################################################################
# _______ _ __ __ _______ _ __ #
# |__ __| | | | \/ | |__ __| | | / _| #
# | |_ __ __ _ ___| | ___ _| \ / | ___| |_ __ __ _ ___| | _| |_ __ _ ___ ___ #
# | | '__/ _` |/ __| |/ / | | | |\/| |/ __| | '__/ _` |/ __| |/ / _/ _` |/ __/ _ \ #
# | | | | (_| | (__| <| |_| | | | | (__| | | | (_| | (__| <| || (_| | (_| __/ #
# |_|_| \__,_|\___|_|\_\\__, |_| |_|\___|_|_| \__,_|\___|_|\_\_| \__,_|\___\___| #
# __/ | #
# |___/ #
# #
# #
# IronTract Team #
########################################################################################
#################
# Load the data #
#################
dwi_img = nib.load(dwi_file_path)
bvals, bvecs = read_bvals_bvecs(bvalues_file_path,
bvecs_file_path)
gtab = gradient_table(bvals, bvecs)
############################################
# Extract the brain mask from the b0 image #
############################################
_, brain_mask = median_otsu(dwi_img.get_data()[:, :, :, 0],
median_radius=2, numpass=1)
##################################################################
# Fit the tensor model and compute the fractional anisotropy map #
##################################################################
context.set_progress(message='Processing voxel-wise DTI metrics.')
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(dwi_img.get_data(), mask=brain_mask)
FA = fractional_anisotropy(tenfit.evals)
stopping_criterion = ThresholdStoppingCriterion(FA, 0.2)
sphere = get_sphere("repulsion724")
seed_mask_img = nib.load(inject_file_path)
affine = seed_mask_img.affine
seeds = utils.random_seeds_from_mask(seed_mask_img.get_data(),
affine,
seed_count_per_voxel=True,
seeds_count=5000)
if dataset == "HCPL":
################################################
# Compute Fiber Orientation Distribution (CSD) #
################################################
context.set_progress(message='Processing voxel-wise FOD estimation.')
response, _ = auto_response_ssst(gtab, dwi_img.get_data(),
roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
csd_fit = csd_model.fit(dwi_img.get_data(), mask=brain_mask)
shm = csd_fit.shm_coeff
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(shm,
max_angle=20.,
sphere=sphere,
pmf_threshold=0.1)
elif dataset == "DSI":
context.set_progress(message='Processing voxel-wise DSI estimation.')
dsmodel = DiffusionSpectrumModel(gtab)
dsfit = dsmodel.fit(dwi_img.get_data())
ODFs = dsfit.odf(sphere)
prob_dg = ProbabilisticDirectionGetter.from_pmf(ODFs,
max_angle=20.,
sphere=sphere,
pmf_threshold=0.01)
###########################################
# Compute DIPY Probabilistic Tractography #
###########################################
context.set_progress(message='Processing tractography.')
streamline_generator = LocalTracking(prob_dg, stopping_criterion, seeds,
affine, step_size=.2, max_cross=1)
streamlines = Streamlines(streamline_generator)
# sft = StatefulTractogram(streamlines, seed_mask_img, Space.RASMM)
# streamlines_file_path = "/root/streamlines.trk"
# save_trk(sft, streamlines_file_path)
###########################################################################
# Compute 3D volumes for the IronTract Challenge. For 'EPFL', we only #
# keep streamlines with length > 1mm. We compute the visitation count #
# image and apply a small gaussian smoothing. The gaussian smoothing #
# is especially usefull to increase voxel coverage of deterministic #
# algorithms. The log of the smoothed visitation count map is then #
# iteratively thresholded producing 200 volumes/operation points. #
# For VUMC, additional streamline filtering is done using anatomical #
# priors (keeping only streamlines that intersect with at least one ROI). #
###########################################################################
if postprocessing in ["EPFL", "ALL"]:
context.set_progress(message='Processing density map (EPFL)')
volume_folder = "/root/vol_epfl"
output_epfl_zip_file_path = "/root/TrackyMcTrackface_EPFL_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(volume_folder,
"vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_epfl_zip_file_path[:-4], 'zip', volume_folder)
if postprocessing in ["VUMC", "ALL"]:
context.set_progress(message='Processing density map (VUMC)')
ROIs_img = nib.load(VUMC_ROIs_file_path)
volume_folder = "/root/vol_vumc"
output_vumc_zip_file_path = "/root/TrackyMcTrackface_VUMC_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
rois = ROIs_img.get_fdata().astype(int)
_, grouping = utils.connectivity_matrix(streamlines, affine, rois,
inclusive=True,
return_mapping=True,
mapping_as_streamlines=False)
streamlines = streamlines[grouping[(0, 1)]]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(volume_folder,
"vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_vumc_zip_file_path[:-4], 'zip', volume_folder)
###################
# Upload the data #
###################
context.set_progress(message='Uploading results...')
#context.upload_file(fa_file_path, 'fa.nii.gz')
# context.upload_file(fod_file_path, 'fod.nii.gz')
# context.upload_file(streamlines_file_path, 'streamlines.trk')
if postprocessing in ["EPFL", "ALL"]:
context.upload_file(output_epfl_zip_file_path,
'TrackyMcTrackface_' + dataset +'_EPFL.zip')
if postprocessing in ["VUMC", "ALL"]:
context.upload_file(output_vumc_zip_file_path,
'TrackyMcTrackface_' + dataset +'_VUMC.zip')
def create_density_map(dwi_img, dir_path, streamlines, conn_model,
target_samples, node_size, curv_thr_list, step_list,
network, roi, directget, max_length):
"""
Create a density map of the list of streamlines.
Parameters
----------
dwi_img : Nifti1Image
Dwi data stored as a Nifti1image object.
dir_path : str
Path to directory containing subject derivative data for a given pynets run.
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
target_samples : int
Total number of streamline samples specified to generate streams.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
max_length : int
Maximum fiber length threshold in mm to restrict tracking.
Returns
-------
streams : str
File path to saved streamline array sequence in DTK-compatible trackvis (.trk) format.
dir_path : str
Path to directory containing subject derivative data for a given pynets run.
dm_path : str
File path to fiber density map Nifti1Image.
"""
import os
import os.path as op
from dipy.tracking import utils
from dipy.io.stateful_tractogram import Space, StatefulTractogram, Origin
from dipy.io.streamline import save_tractogram
# Create density map
dm = utils.density_map(streamlines,
affine=np.eye(4),
vol_dims=dwi_img.shape)
# Save density map
dm_img = nib.Nifti1Image(dm.astype('int'), dwi_img.affine)
namer_dir = '{}/tractography'.format(dir_path)
if not os.path.isdir(namer_dir):
os.mkdir(namer_dir)
dm_path = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir, '/density_map_', '%s' %
(network + '_' if network is not None else ''), '%s' %
(op.basename(roi).split('.')[0] + '_' if roi is not None else ''),
conn_model, '_', target_samples, '_', '%s' %
("%s%s" % (node_size, 'mm_') if
((node_size != 'parc') and (node_size is not None)) else 'parc_'),
'curv-', str(curv_thr_list).replace(
', ', '_'), '_step-', str(step_list).replace(
', ', '_'), '_dg-', directget, '_ml-', max_length, '.nii.gz')
dm_img.to_filename(dm_path)
# Save streamlines to trk
streams = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir, '/streamlines_', '%s' %
(network + '_' if network is not None else ''), '%s' %
(op.basename(roi).split('.')[0] + '_' if roi is not None else ''),
conn_model, '_', target_samples, '_', '%s' %
("%s%s" % (node_size, 'mm_') if
((node_size != 'parc') and (node_size is not None)) else 'parc_'),
'curv-', str(curv_thr_list).replace(
', ', '_'), '_step-', str(step_list).replace(
', ', '_'), '_dg-', directget, '_ml-', max_length, '.trk')
save_tractogram(StatefulTractogram(streamlines,
reference=dwi_img,
space=Space.RASMM,
origin=Origin.TRACKVIS),
streams,
bbox_valid_check=False)
del streamlines
dm_img.uncache()
return streams, dir_path, dm_path
def create_density_map(
dwi_img,
dir_path,
streamlines,
conn_model,
target_samples,
node_size,
curv_thr_list,
step_list,
network,
roi,
directget,
min_length,
namer_dir,
):
"""
Create a density map of the list of streamlines.
Parameters
----------
dwi_img : Nifti1Image
Dwi data stored as a Nifti1image object.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
conn_model : str
Connectivity reconstruction method (e.g. 'csa', 'tensor', 'csd').
target_samples : int
Total number of streamline samples specified to generate streams.
node_size : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's for tracking.
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default')
used to filter nodes in the study of brain subgraphs.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
directget : str
The statistical approach to tracking. Options are: det (deterministic),
closest (clos), boot (bootstrapped), and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
Returns
-------
streams : str
File path to saved streamline array sequence in DTK-compatible
trackvis (.trk) format.
dir_path : str
Path to directory containing subject derivative data for a given
pynets run.
dm_path : str
File path to fiber density map Nifti1Image.
"""
import os.path as op
from dipy.tracking import utils
# Create density map
dm = utils.density_map(streamlines,
affine=np.eye(4),
vol_dims=dwi_img.shape)
# Save density map
dm_img = nib.Nifti1Image(dm.astype("float32"), dwi_img.affine)
dm_path = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir,
"/density_map_",
"%s" % (network + "_" if network is not None else ""),
"%s" %
(op.basename(roi).split(".")[0] + "_" if roi is not None else ""),
conn_model,
"_",
target_samples,
"_",
"%s" % ("%s%s" % (node_size, "mm_") if
((node_size != "parc") and
(node_size is not None)) else "parc_"),
"curv-",
str(curv_thr_list).replace(", ", "_"),
"_step-",
str(step_list).replace(", ", "_"),
"_directget-",
directget,
"_minlength-",
min_length,
".nii.gz",
)
dm_img.to_filename(dm_path)
del streamlines
dm_img.uncache()
return dir_path, dm_path
def _run_interface(self, runtime):
# Loading the ROI file
from dipy.tracking import utils
import nibabel as nib
import numpy as np
import os
img = nib.load(self.inputs.ROI_file)
data = img.get_data()
affine = img.get_affine()
# Getting ROI volumes if they haven't been generated
if not os.path.isfile('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv'):
import nibabel as nib
import numpy as np
import os
import pandas as pd
import subprocess
atlas_file = ROI_file
img = nib.load(atlas_file)
data = img.get_data()
affine = img.get_affine()
volumes = pd.DataFrame()
atlas_labels = np.unique(data)
for atlas_label in atlas_labels:
data = nib.load(atlas_file).get_data()
data[data != atlas_label] = 0
data[data == atlas_label] = 1
nib.save(nib.Nifti1Image(data, affine), 'temp.nii.gz')
volumes.set_value(atlas_label, 'volume', subprocess.check_output(os.environ['FSLDIR'] + '/bin/fslstats temp.nii.gz -V', shell=True).split(' ')[0])
os.remove('temp.nii.gz')
volumes.to_csv('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')
ROI_volumes = pd.read_csv('/home/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')
# Getting the FA file
img = nib.load(self.inputs.FA_file)
FA_data = img.get_data()
FA_affine = img.get_affine()
# Loading the streamlines
from nibabel import trackvis
streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
streamlines = [s[0] for s in streams]
streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)
# Checking for negative values
from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
endpoints = [sl[0::len(sl)-1] for sl in streamlines]
lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
inds = np.dot(endpoints, lin_T)
inds += offset
negative_values = np.where(inds <0)[0]
for negative_value in sorted(negative_values, reverse=True):
del streamlines[negative_value]
# Constructing the streamlines matrix
matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
matrix[matrix < 10] = 0
# Constructing the FA matrix
dimensions = matrix.shape
FA_matrix = np.empty(shape=dimensions)
density_matrix = np.empty(shape=dimensions)
density_corrected_matrix = np.empty(shape=dimensions)
for i in range(0,dimensions[0]):
for j in range(0,dimensions[1]):
if matrix[i,j]:
dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
FA_matrix[i,j] = np.mean(FA_data[dm>0])
if np.sum(dm > 0) > 0:
density_matrix[i,j] = np.sum(dm[dm > 0])
density_corrected_matrix[i,j] = np.sum(dm[dm > 0])/np.sum([ROI_volumes.iloc[i].values.astype('int'), ROI_volumes.iloc[j].values.astype('int')])
else:
density_matrix[i,j] = 0
density_corrected_matrix[i,j] = 0
else:
FA_matrix[i,j] = 0
density_matrix[i,j] = 0
density_corrected_matrix[i,j] = 0
FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)
density_matrix[np.tril_indices(n=len(density_matrix))] = 0
density_matrix = density_matrix.T + density_matrix - np.diagonal(density_matrix)
density_corrected_matrix[np.tril_indices(n=len(density_corrected_matrix))] = 0
density_corrected_matrix = density_corrected_matrix.T + density_corrected_matrix - np.diagonal(density_corrected_matrix)
from nipype.utils.filemanip import split_filename
_, base, _ = split_filename(self.inputs.trackfile)
np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
np.savetxt(base + '_density_matrix.txt',density_matrix,delimiter='\t')
np.savetxt(base + '_volume_corrected_density_matrix.txt',density_corrected_matrix,delimiter='\t')
from dipy.tracking.vox2track import track_counts
from dipy.tracking.utils import density_map
import nibabel as nib
from nibabel.trackvis import write, empty_header
grid = np.mgrid[1.1:1.8:3j,1.1:1.8:3j,.5:5]
grid = np.rollaxis(grid, 0, 4)
streamlines = []
for ii in grid:
for jj in ii:
streamlines.append(jj)
#Treat these streamlines as if they are in trackvis format and generate counts
counts_trackvis = density_map(streamlines, (4,4,5), (1,1,1))
#Treat these streamlines as if they are in nifti format and generate counts
counts_nifti = track_counts(streamlines, (4,4,5), (1,1,1),
return_elements=False)
print("saving trk files and track_count volumes")
aff = np.eye(4)
aff[0, 0] = -1
img = nib.Nifti1Image(counts_trackvis.astype('int16'), aff)
nib.save(img, 'counts_trackvis.nii.gz')
img = nib.Nifti1Image(counts_nifti.astype('int16'), aff)
nib.save(img, 'counts_nifti.nii.gz')
hdr = empty_header()
hdr['voxel_size'] = (1,1,1)