def test_normal_execution(self, mock_loadtxt):
""" Test the normal behaviour of the function.
"""
# Set the mocked functions returned values
bvecs = numpy.array([[0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]]).T
bvals = numpy.array([0, 0, 1500, 150])
mock_loadtxt.side_effect = lambda x: {"dwi.bval": bvals,
"dwi.bvec": bvecs}[x]
# Test execution
output_files = read_bvals_bvecs("dwi.bval", "dwi.bvec", min_bval=100.)
self.assertTrue(numpy.allclose(output_files[0], bvals))
self.assertTrue(numpy.allclose(output_files[1], bvecs.T))
self.assertTrue(output_files[2] == 2)
self.assertTrue(output_files[3] == 2)
def test_normal_execution(self, mock_loadtxt):
""" Test the normal behaviour of the function.
"""
# Set the mocked functions returned values
bvecs = numpy.array([[0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]]).T
bvals = numpy.array([0, 0, 1500, 150])
mock_loadtxt.side_effect = lambda x: {
"dwi.bval": bvals,
"dwi.bvec": bvecs
}[x]
# Test execution
output_files = read_bvals_bvecs("dwi.bval", "dwi.bvec", min_bval=100.)
self.assertTrue(numpy.allclose(output_files[0], bvals))
self.assertTrue(numpy.allclose(output_files[1], bvecs.T))
self.assertTrue(output_files[2] == 2)
self.assertTrue(output_files[3] == 2)
def concatenate_volumes(nii_files, bvals_files, bvecs_files, outdir, axis=-1):
""" Concatenate volumes of different nifti files.
Parameters
----------
nii_files: array of str
array containing the different nii files to concatenate.
bvals_files: list of str
path to the diffusion b-values files.
bvecs_files: list of str
path to the diffusion b-vectors files.
outdir: str
subject output directory.
axis: int, default -1
the concatenation axis.
Returns
-------
dwi_file: str
path to the concatenated nii files.
bval_file: str
path to the concatenated bval files.
bvec_file: str
path to the concatenated bvec files.
"""
# Concatenate volumes
data = []
affines = []
for path in nii_files:
im = nibabel.load(path)
data.append(im.get_data())
affines.append(im.affine)
concatenated_volumes = numpy.concatenate(data, axis=axis)
# Check that affine are the same between volumes
ref_affine = affines[0]
for aff in affines:
if not numpy.allclose(ref_affine, aff):
raise ValueError("Different affines between DWI volumes: {0}"
"...".format(nii_files))
# Read the bvals and bvecs
bvals, bvecs, nb_shells, nb_nodiff = read_bvals_bvecs(
bvals_files, bvecs_files, min_bval=200)
if nb_nodiff > 1:
nodiff_indexes = numpy.where(bvals <= 50)[0].tolist()
b0_array = concatenated_volumes[..., nodiff_indexes[0]]
b0_array.shape += (1, )
cpt_delete = 0
for i in nodiff_indexes:
concatenated_volumes = numpy.delete(
concatenated_volumes, i - cpt_delete, axis=3)
bvals = numpy.delete(bvals, i - cpt_delete, axis=0)
bvecs = numpy.delete(bvecs, i - cpt_delete, axis=0)
cpt_delete += 1
concatenated_volumes = numpy.concatenate(
(b0_array, concatenated_volumes), axis=3)
bvals = numpy.concatenate((numpy.array([0]), bvals), axis=0)
bvecs = numpy.concatenate((numpy.array([[0, 0, 0]]), bvecs), axis=0)
# Save the results
dwi_file = os.path.join(outdir, "dwi.nii.gz")
bval_file = os.path.join(outdir, "dwi.bval")
bvec_file = os.path.join(outdir, "dwi.bvec")
concatenated_nii = nibabel.Nifti1Image(concatenated_volumes, ref_affine)
nibabel.save(concatenated_nii, dwi_file)
bvals.shape += (1, )
numpy.savetxt(bval_file, bvals.T, fmt="%f")
numpy.savetxt(bvec_file, bvecs.T, fmt="%f")
return dwi_file, bval_file, bvec_file
def mrtrix_tractogram(
outdir,
tempdir,
subject_id,
dwi,
bvals,
bvecs,
nb_threads,
global_tractography=False,
mtracks=None,
maxlength=None,
cutoff=None,
seed_gmwmi=False,
sift_mtracks=None,
sift2=False,
nodif_brain=None,
nodif_brain_mask=None,
fast_t1_brain=None,
subjects_dir=None,
mif_gz=True,
delete_raw_tracks=False,
delete_dwi_mif=True,
fs_sh=DEFAULT_FREESURFER_PATH,
fsl_sh=DEFAULT_FSL_PATH):
"""
Compute the connectome using MRtrix.
Requirements:
- FreeSurfer: result of recon-all on the T1.
- a T1 parcellation that defines the nodes of the connectome, it has
to be in the FreeSurfer space (i.e. aligned with
<subjects dir>/<subject>/mri/brain.mgz), e.g. aparc+aseg from
FreeSurfer.
Parameters
----------
outdir: str
Path to directory where to output.
tempdir: str
Path to the directory where temporary directories should be written.
It should be a partition with 5+ GB available.
subject_id: str
Subject identifier.
dwi: str
Path to the diffusion-weighted images (Nifti required).
bvals: str
Path to the bvalue list.
bvecs: str
Path to the list of diffusion-sensitized directions.
nb_threads: int
Number of threads.
global_tractography: bool, default False
If True run global tractography (tckglobal) instead of local (tckgen).
mtracks: int, default None
For non-global tractography only. Number of millions of tracks of the
raw tractogram.
maxlength: int, default None
For non-global tractography only. Max fiber length in mm.
cutoff: float, default None
For non-global tractography only.
FOD amplitude cutoff, stopping criteria.
seed_gmwmi: bool, default False
For non-global tractography only.
Set this option if you want to activate the '-seed_gmwmi' option of
MRtrix 'tckgen', to seed from the GM/WM interface. Otherwise, and by
default, the seeding is done in white matter ('-seed_dynamic' option).
sift_mtracks: int, default None
For non-global tractography only.
Number of millions of tracks to keep with SIFT.
If not set, SIFT is not applied.
sift2: bool, default False
For non-global tractography only.
To activate SIFT2.
nodif_brain: str, default None
Diffusion brain-only Nifti volume with bvalue ~ 0. If not passed, it is
generated automatically by averaging all the b0 volumes of the DWI.
nodif_brain_mask: str, default None
Path to the Nifti brain binary mask in diffusion. If not passed, it is
created with MRtrix 'dwi2mask'.
fast_t1_brain: str, default None
By default FSL FAST is run on the FreeSurfer 'brain.mgz'. If you want
the WM probability map to be computed from another T1, pass the T1
brain-only volume. Note that it has to be aligned with diffusion.
This argument is useful for HCP, where some FreeSurfer 'brain.mgz'
cannot be processed by FSL FAST.
subjects_dir: str, default None
Path to the FreeSurfer subjects directory. Required if the environment
variable $SUBJECTS_DIR is not set.
mif_gz: bool, default True
Use compressed MIF files (.mif.gz) instead of .mif to save space.
delete_raw_tracks: bool, default False
Delete the raw tracks (<outdir>/<mtracks>M.tck) at the end of
processing, to save space.
delete_dwi_mif: bool, default True
Delete <outdir>/DWI.mif(.gz) at the end of processing, which is a copy
of the input <dwi> in the MIF format, to save space.
fs_sh: str, default NeuroSpin path
Path to the Bash script setting the FreeSurfer environment
fsl_sh: str, default NeuroSpin path
Path to the Bash script setting the FSL environment.
Returns
-------
tracks: str
The generated tractogram.
sift_tracks: str
The SIFT filtered tractogram.
sift2_weights: str
The SIFT2 tractogram associated weights.
"""
# -------------------------------------------------------------------------
# STEP 0 - Check arguments
# FreeSurfer $SUBJECTS_DIR has to be passed or set as an env variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Use compressed MIF files or not
MIF_EXT = ".mif.gz" if mif_gz else ".mif"
# Is SIFT to be applied
sift = (sift_mtracks is not None)
# Check input and optional paths
paths_to_check = [dwi, bvals, bvecs, fsl_sh]
for p in [nodif_brain, nodif_brain_mask, fast_t1_brain]:
if p is not None:
paths_to_check.append(p)
for p in paths_to_check:
if not os.path.exists(p):
raise ValueError("File or directory does not exist: %s" % p)
# Identify whether the DWI acquisition is single or multi-shell
_, _, nb_shells, _ = read_bvals_bvecs(bvals, bvecs, min_bval=200.)
is_multi_shell = nb_shells > 1
# Check compatibility of arguments
if global_tractography:
if not is_multi_shell:
raise ValueError("MRtrix global tractography is only applicable "
"to multi shell data.")
if seed_gmwmi:
raise ValueError("'seed_gmwmi' cannot be applied when requesting "
"global tractography.")
if sift or sift2:
raise ValueError("SIFT or SIFT2 are not meant to be used with "
"global tractography.")
else:
value_of_required_arg = dict(mtracks=mtracks, maxlength=maxlength,
cutoff=cutoff)
for required_arg, value in value_of_required_arg.items():
if value is None:
raise ValueError("When 'global_tractography' is set to False "
"%s is required." % required_arg)
# Create <outdir> and/or <tempdir> if not existing
for directory in [outdir, tempdir]:
if not os.path.isdir(directory):
os.makedirs(directory)
# -------------------------------------------------------------------------
# STEP 1 - Format DWI and compute nodif brain and nodif brain mask if
# not provided
# Convert DWI to MRtrix format
dwi_mif = os.path.join(outdir, "DWI.mif")
cmd_1a = ["mrconvert", dwi, dwi_mif, "-fslgrad", bvecs, bvals,
"-datatype", "float32", "-stride", "0,0,0,1",
"-nthreads", "%i" % nb_threads, "-failonwarn"]
subprocess.check_call(cmd_1a)
# If user has not provided a 'nodif_brain_mask', compute one with
# MRtrix 'dwi2mask'
if nodif_brain_mask is None:
nodif_brain_mask = os.path.join(outdir, "nodif_brain_mask.nii.gz")
cmd_1b = ["dwi2mask", dwi_mif, nodif_brain_mask]
subprocess.check_call(cmd_1b)
# If user has not provided a 'nodif_brain', apply 'nodif_brain_mask' to
# mean b=0 volume
if nodif_brain is None:
# Extract b=0 volumes and compute mean b=0 volume
b0s = os.path.join(outdir, "b0s.nii.gz")
mean_b0 = os.path.join(outdir, "mean_b0.nii.gz")
mrtrix_extract_b0s_and_mean_b0(dwi=dwi_mif, b0s=b0s, mean_b0=mean_b0,
nb_threads=nb_threads)
# Apply nodif_brain_mask to dwi
nodif_brain = os.path.join(outdir, "nodif_brain.nii.gz")
cmd_1b = ["mri_mask", mean_b0, nodif_brain_mask, nodif_brain]
FSWrapper(cmd_1b, shfile=fs_sh)()
# -------------------------------------------------------------------------
# STEP 2 - Register DWI to T1 using FreeSurfer bbregister
# - compute the rigid transformation
# - apply transformation to align T1 without downsampling
t1_brain_to_dif, dif2anat_dat, _ = freesurfer_bbregister_t1todif(
outdir=outdir,
subject_id=subject_id,
nodif_brain=nodif_brain,
subjects_dir=subjects_dir,
fs_sh=fs_sh,
fsl_sh=fsl_sh)
# -------------------------------------------------------------------------
# STEP 3 - "5 tissue types" segmentation
# Generate the 5TT image based on a FSL FAST
five_tissues = os.path.join(outdir, "5TT%s" % MIF_EXT)
fast_t1_brain = t1_brain_to_dif if fast_t1_brain is None else fast_t1_brain
cmd_3 = ["5ttgen", "fsl", fast_t1_brain, five_tissues, "-premasked",
"-tempdir", tempdir, "-nthreads", "%i" % nb_threads]
FSLWrapper(cmd_3, env=os.environ, shfile=fsl_sh)()
# -------------------------------------------------------------------------
# STEP 4 - Estimation of the response function of fibers in each voxel
if is_multi_shell:
rf_wm = os.path.join(outdir, "RF_WM.txt")
rf_gm = os.path.join(outdir, "RF_GM.txt")
rf_csf = os.path.join(outdir, "RF_CSF.txt")
cmd_4 = ["dwi2response", "msmt_5tt", dwi_mif, five_tissues,
rf_wm, rf_gm, rf_csf]
else:
rf = os.path.join(outdir, "RF.txt")
cmd_4 = ["dwi2response", "tournier", dwi_mif, rf]
rf_voxels = os.path.join(outdir, "RF_voxels%s" % MIF_EXT)
cmd_4 += ["-voxels", rf_voxels, "-tempdir", tempdir,
"-nthreads", "%i" % nb_threads]
subprocess.check_call(cmd_4)
# -------------------------------------------------------------------------
# STEP 5 - Compute FODs
wm_fods = os.path.join(outdir, "WM_FODs%s" % MIF_EXT)
if is_multi_shell:
gm_mif = os.path.join(outdir, "GM%s" % MIF_EXT)
csf_mif = os.path.join(outdir, "CSF%s" % MIF_EXT)
cmd_5 = ["dwi2fod", "msmt_csd", dwi_mif, rf_wm, wm_fods,
rf_gm, gm_mif, rf_csf, csf_mif]
else:
cmd_5 = ["dwi2fod", "csd", dwi_mif, rf, wm_fods]
cmd_5 += ["-mask", nodif_brain_mask, "-nthreads", "%i" % nb_threads,
"-failonwarn"]
subprocess.check_call(cmd_5)
# -------------------------------------------------------------------------
# STEP 6 - Image to visualize for multi-shell
if is_multi_shell:
wm_fods_vol0 = os.path.join(outdir, "WM_FODs_vol0%s" % MIF_EXT)
cmd_6a = ["mrconvert", wm_fods, wm_fods_vol0, "-coord", "3", "0",
"-nthreads", "%i" % nb_threads]
subprocess.check_call(cmd_6a)
tissueRGB_mif = os.path.join(outdir, "tissueRGB%s" % MIF_EXT)
cmd_6b = ["mrcat", csf_mif, gm_mif, wm_fods_vol0, tissueRGB_mif,
"-axis", "3", "-nthreads", "%i" % nb_threads, "-failonwarn"]
subprocess.check_call(cmd_6b)
# -------------------------------------------------------------------------
# STEP 7 - Tractography: tckglobal or tckgen
if global_tractography:
tracks = os.path.join(outdir, "global.tck")
global_fod = os.path.join(outdir, "fod%s" % MIF_EXT)
fiso_mif = os.path.join(outdir, "fiso%s" % MIF_EXT)
cmd_7 = ["tckglobal", dwi_mif, rf_wm, "-riso", rf_csf, "-riso", rf_gm,
"-mask", nodif_brain_mask, "-niter", "1e8",
"-fod", global_fod, "-fiso", fiso_mif, tracks]
else:
# Anatomically Constrained Tractography:
# iFOD2 algorithm with backtracking and crop fibers at GM/WM interface
tracks = os.path.join(outdir, "%iM.tck" % mtracks)
cmd_7 = ["tckgen", wm_fods, tracks, "-act", five_tissues, "-backtrack",
"-crop_at_gmwmi", "-maxlength", "%i" % maxlength,
"-number", "%dM" % mtracks, "-cutoff", "%f" % cutoff]
# Requested seeding strategy: -seed_gmwmi or -seed_dynamic
if seed_gmwmi:
gmwmi_mask = os.path.join(outdir, "gmwmi_mask%s" % MIF_EXT)
cmd_7b = ["5tt2gmwmi", five_tissues, gmwmi_mask]
subprocess.check_call(cmd_7b)
cmd_7 += ["-seed_gmwmi", gmwmi_mask]
else:
cmd_7 += ["-seed_dynamic", wm_fods]
cmd_7 += ["-nthreads", "%i" % nb_threads, "-failonwarn"]
subprocess.check_call(cmd_7)
# -------------------------------------------------------------------------
# STEP 8 - Filter tracts with SIFT if requested
sift_tracks = None
if sift:
sift_tracks = os.path.join(outdir, "%iM_SIFT.tck" % sift_mtracks)
cmd_8 = ["tcksift", tracks, wm_fods, sift_tracks,
"-act", five_tissues, "-term_number", "%iM" % sift_mtracks,
"-nthreads", "%i" % nb_threads, "-failonwarn"]
subprocess.check_call(cmd_8)
# -------------------------------------------------------------------------
# STEP 9 - run SIFT2 if requested (compute weights of fibers)
sift2_weights = None
if sift2:
sift2_weights = os.path.join(outdir, "sift2_weights.txt")
cmd_9 = ["tcksift2", tracks, wm_fods, sift2_weights,
"-act", five_tissues,
"-nthreads", "%i" % nb_threads, "-failonwarn"]
subprocess.check_call(cmd_9)
# -------------------------------------------------------------------------
# STEP 10 - clean if requested
if delete_raw_tracks:
os.remove(tracks)
tracks = None
if delete_dwi_mif:
os.remove(dwi_mif)
dwi_mif = None
else:
if mif_gz:
subprocess.check_call(["gzip", dwi_mif])
dwi_mif += ".gz"
return tracks, sift_tracks, sift2_weights
def data_import_and_qspace_sampling(
outdir,
subject_id,
dwis,
bvals,
bvecs,
manufacturer,
flipX=False,
flipY=False,
flipZ=False,
invertX=True,
invertY=False,
invertZ=False,
b0_magnitude=None,
b0_phase=None,
phase_axis="y",
slice_axis="z",
path_connectomist=DEFAULT_CONNECTOMIST_PATH):
""" Wrapper to Connectomist's 'DWI & Q-space' tab.
Parameters
----------
outdir: str
path to Connectomist's output directory.
subject_id: str
the subject code in study.
dwis: list of str
path to Nifti diffusion-weighted datasets.
bvals: list of str
path to .bval files associated to the Nifti.
bvecs: list of str
path to .bvec files associated to the Nifti.
manufacturer: str
name of the manufacturer (e.g. "Siemens", "GE", "Philips" or "Bruker").
flipX, flipY, flipZ: bool (optional, default False)
if True invert the x, y or z-axis of data.
invertX: bool (optional, default True)
if True invert x-axis of diffusion model.
invertY, invertZ: bool (optional, default False)
if True invert y or z-axis of diffusion model.
b0_magnitude: str (optional, default None)
path to the magnitude fieldmap (if fieldmap-based correction of
susceptibility distortions is to be used).
b0_phase: str (optional, default None)
path to phase fieldmap (if fieldmap-based correction of susceptibility
distortions is to be used).
phase_axis: str (optional, default 'y')
the acquistion phase axis 'x', 'y' or 'z'.
slice_axis: str (optional, default 'z')
the acquistion slice axis 'x', 'y' or 'z'.
path_connectomist: str (optional)
path to the Connectomist executable.
Returns
-------
outdir: str
path to Connectomist's output directory.
"""
# Check input parameters
for axis in (phase_axis, slice_axis):
if axis not in AXIS:
raise ValueError("Invalid axis '{0}'.".format(axis))
if len(set(map(len, (dwis, bvecs, bvals)))) != 1:
raise ValueError("The DWIs, BVECs and BVALs must have the same number "
"of elements.")
# Create the Connectomist's import data directory
dwis, bvals, bvecs, b0_magnitude, b0_phase = gather_and_format_input_files(
outdir,
dwis,
bvals,
bvecs,
b0_magnitude,
b0_phase)
# Dict with all parameters for connectomist
algorithm = "DWI-Data-Import-And-QSpace-Sampling"
parameters_dict = {
# Parameters are ordered as they appear in connectomist's GUI
# ---------------------------------------------------------------------
# Field: "Diffusion weighted-images"
"fileNameDwi": ";".join(dwis), # "DW data"
"sliceAxis": AXIS[slice_axis], # "Slice axis", default "Z-axis"
"phaseAxis": AXIS[phase_axis], # "Phase axis", default "Y-axis"
"manufacturer": None,
# Subfield: "Advanced parameters"
"flipAlongX": 2 if flipX else 0, # "Flip data along x"
"flipAlongY": 2 if flipY else 0,
"flipAlongZ": 2 if flipZ else 0,
"numberOfDiscarded": 0, # "#discarded images at beginning"
"numberOfT2": None, # "#T2"
"numberOfRepetitions": 1, # "#repetitions"
# ---------------------------------------------------------------------
# Field: "Rotation of field of view", default is identity matrix
"qSpaceTransform_xx": 1.0,
"qSpaceTransform_xy": 0.0,
"qSpaceTransform_xz": 0.0,
"qSpaceTransform_yx": 0.0,
"qSpaceTransform_yy": 1.0,
"qSpaceTransform_yz": 0.0,
"qSpaceTransform_zx": 0.0,
"qSpaceTransform_zy": 0.0,
"qSpaceTransform_zz": 1.0,
# ---------------------------------------------------------------------
# Field: "Q-space sampling"
"qSpaceSamplingType": 4, # default "spherical single-shell custom"
"qSpaceChoice5BValueFileNames": ";".join(bvals),
"qSpaceChoice5BValueThreshold": 50,
"qSpaceChoice5OrientationFileNames": ";".join(bvecs),
# Apparently Connectomist uses 2 as True, and 0 as False.
"invertXAxis": 2 if invertX else 0,
"invertYAxis": 2 if invertY else 0,
"invertZAxis": 2 if invertZ else 0,
# In this field but not used/handled parameters
"qSpaceChoice1MaximumBValue": 1000, # case Cartesian
"qSpaceChoice2BValue": 1000,
"qSpaceChoice3BValue": 1000,
"qSpaceChoice4BValue": 1000,
"qSpaceChoice6BValues": "",
"qSpaceChoice7BValues": "",
"qSpaceChoice8BValues": "",
"qSpaceChoice9BValues": "",
"qSpaceChoice10BValues": "",
"qSpaceChoice11BValues": "",
"qSpaceChoice1NumberOfSteps": 11,
"qSpaceChoice2NumberOfOrientations": 6,
"qSpaceChoice3NumberOfOrientations": 6,
"qSpaceChoice4NumberOfOrientations": 6,
"qSpaceChoice6NumberOfOrientations": 6,
"qSpaceChoice7NumberOfOrientations": 6,
"qSpaceChoice8NumberOfOrientations": 6,
"qSpaceChoice10NumberOfOrientations": "",
"qSpaceChoice11NumberOfOrientations": "",
"qSpaceChoice13OrientationFileNames": "",
# ---------------------------------------------------------------------
# Field: micro structure config"
"gradientCharacteristicsFileNames": "",
# ---------------------------------------------------------------------
# Field: "Work directory"
"outputWorkDirectory": outdir,
# ---------------------------------------------------------------------
# unknown parameter
"_subjectName": subject_id,
}
# Map the manufacturer name with Connectomist convention
if manufacturer not in MANUFACTURERS:
raise ConnectomistBadManufacturerNameError(manufacturer)
parameters_dict["manufacturer"] = MANUFACTURERS[manufacturer]
# Read bvals and bvecs
bvalues, bvectors, nb_shells, nb_nodiff = read_bvals_bvecs(bvals, bvecs)
# Update Connectomist step description
parameters_dict["numberOfT2"] = nb_nodiff
if nb_shells == 1:
# Spherical single-shell custom
parameters_dict["qSpaceSamplingType"] = 4
else:
warnings.warn(
"'{0}' shell model(s) not handled yet.".format(nb_shells))
# Arbitrary shell
parameters_dict["qSpaceSamplingType"] = 12
parameters_dict["qSpaceChoice13BValueFileNames"] = ";".join(bvals)
parameters_dict["qSpaceChoice13BValueThreshold"] = 50.0
parameters_dict["qSpaceChoice13OrientationFileNames"] = ";".join(bvecs)
# Call with Connectomist
process = ConnectomistWrapper(path_connectomist)
parameter_file = ConnectomistWrapper.create_parameter_file(
algorithm, parameters_dict, outdir)
process(algorithm, parameter_file, outdir)
# When there are multiple volume or when there are multiple t2 (nodif)
# volumes, Connectomist merges them
# rewrite bvec, bval file accordingly (remove extra T2 values)
if nb_nodiff > 1:
bval = os.path.join(outdir, "dwi.bval")
dw_indexes = np.where(bvalues >= 100)[0]
new_bvals = np.concatenate(([0], bvalues[dw_indexes]))
np.savetxt(bval, new_bvals)
bvec = os.path.join(outdir, "dwi.bvec")
new_bvecs = np.concatenate(
([[0], [0], [0]], bvectors.T[:, dw_indexes]), axis=1)
np.savetxt(bvec, new_bvecs)
return outdir
def trac_all_longitudinal(outdir,
subject_template_id,
subject_timepoint_ids,
dwis,
bvalss,
bvecss,
bedpostx_dirs,
subjects_dir=None,
do_eddy=False,
do_rotate_bvecs=True,
do_bbregister=True,
do_register_mni=True,
temp_dir=None,
fsconfig=DEFAULT_FREESURFER_PATH):
"""
Parameters
----------
outdir: str
Root directory where to create the subject's output directories.
Created if not existing.
subject_template_id: str
Identifier of the subject template.
subject_timepoint_ids: str
Identifiers of the subject for all the timepoints.
dwis: list of str
Paths to Nifti diffusion series. In the order corresponding to
<subject_timepoint_ids>.
bvalss: list of str
Paths to b-values of diffusion series. In the order corresponding to
<dwis>.
bvecss: list of str
Paths to diffusion-sensitized directions of diffusion series. In the
order corresponding to <dwis>.
bedpostx_dirs: list of str
BedpostX output directories. In the order corresponding to <dwis>.
subjects_dir: str, default None
Path to the FreeSurfer longitudinal subjects directory. Required if
the environment variable $SUBJECTS_DIR is not set.
do_eddy: bool, default False
Apply FSL eddy-current correction.
do_rotate_bvecs: bool, default True
Rotate bvecs to match eddy-current correction.
do_bbregister: bool, default True
Register diffusion to T1 using bbregister.
do_register_mni:
Register T1 to MNI.
temp_dir: str, default None
Set the root temporary directory.
fsconfig: str, default <pyfreesurfer.DEFAULT_FREESURFER_PATH>
Path to the FreeSurfer configuration file.
Returns
-------
subject_long_outdirs: list of str
Path to longitudinal subject's output directories.
"""
# Check input arguments
# Check that the user has passed non-empty lists of the same length
list_args = [subject_timepoint_ids, dwis, bvalss, bvecss, bedpostx_dirs]
are_all_lists = all(map(lambda x: isinstance(x, list), list_args))
all_same_size = len(set(map(len, list_args))) == 1
non_empty = len(subject_timepoint_ids) > 1
if not (are_all_lists & all_same_size & non_empty):
raise ValueError("'subject_timepoint_ids', 'dwis', 'bvals', 'bvecs' "
"and 'bedpostx_dirs' must be lists of IDs/paths.")
# FreeSurfer $SUBJECTS_DIR has to be passed or set as an env variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Check existence of input files/directories
input_paths = dwis + bvalss + bvecss + bedpostx_dirs + [fsconfig]
for path in input_paths:
if not os.path.exists(path):
raise ValueError("File or directory does not exist: %s" % path)
# Create directory for temporary files
temp_dir = tempfile.mkdtemp(prefix="trac-all_longitudinal_", dir=temp_dir)
# The bvecs can be stored as a 3 x N or N x 3 matrix (N directions).
# FreeSurfer requires the 2nd convention (one row per direction).
# read_bvals_bvecs() always loads the bvecs file as a N x 3 numpy array,
# save this numpy array in a temporary directory and use it as the
# bvecs file to be sure to be in the right convention.
bvecss_Nx3 = []
for tp_sid, bvals, bvecs in zip(subject_timepoint_ids, bvalss, bvecss):
_, bvecs_array, _, _ = read_bvals_bvecs(bvals_path=bvals,
bvecs_path=bvecs,
min_bval=200.)
bvecs_Nx3 = os.path.join(temp_dir, "bvecs_Nx3_%s" % tp_sid)
numpy.savetxt(bvecs_Nx3, bvecs_array)
bvecss_Nx3.append(bvecs_Nx3)
# Create configuration file
str_subjlist = " ".join(subject_timepoint_ids)
str_baselist = " ".join([subject_template_id] * len(subject_timepoint_ids))
str_dwis = " ".join(dwis)
str_bveclist = " ".join(bvecss_Nx3)
str_bvallist = " ".join(bvalss)
config_str = LONG_CONFIG_TEMPLATE.format(subjects_dir=subjects_dir,
subjlist=str_subjlist,
baselist=str_baselist,
dtroot=outdir,
dcmlist=str_dwis,
bveclist=str_bveclist,
bvallist=str_bvallist,
doeddy=do_eddy,
dorotbvecs=do_rotate_bvecs,
doregbbr=do_bbregister,
doregmni=do_register_mni)
path_config = os.path.join(temp_dir, "trac-all.long.dmrirc")
with open(path_config, "w") as f:
f.write(config_str)
# For each timepoint of subject:
subject_long_outdirs = []
for tp_sid, bedpostx_dir in zip(subject_timepoint_ids, bedpostx_dirs):
# Create <outdir>/<tp sid>.long.<template id> if not existing
long_sid = "%s.long.%s" % (tp_sid, subject_template_id)
long_outdir = os.path.join(outdir, long_sid)
if not os.path.isdir(long_outdir):
os.makedirs(long_outdir)
subject_long_outdirs.append(long_outdir)
# Tracula requires the bedpostX files to be stored in
# <outdir>/<tp sid>.long.<template id>/dmri.bedpostX
create_bedpostx_organization(subject_id=long_sid,
subjects_dir=outdir,
bedpostx_dir=bedpostx_dir)
# Run Tracula preparation
cmd_prep = ["trac-all", "-prep", "-c", path_config]
FSWrapper(cmd_prep,
shfile=fsconfig,
subjects_dir=subjects_dir,
add_fsl_env=True)()
# Tracula pathways tractography
cmd_path = ["trac-all", "-path", "-c", path_config]
FSWrapper(cmd_path,
shfile=fsconfig,
subjects_dir=subjects_dir,
add_fsl_env=True)()
# Clean tmp dir
shutil.rmtree(temp_dir)
return subject_long_outdirs
def trac_all(outdir,
subject_id,
dwi,
bvals,
bvecs,
bedpostx_dir,
subjects_dir=None,
do_eddy=False,
do_rotate_bvecs=True,
do_bbregister=True,
do_register_mni=True,
temp_dir=None,
fsconfig=DEFAULT_FREESURFER_PATH):
"""
Parameters
----------
outdir: str
Root directory where to create the subject's output directory.
Created if not existing.
subject_id: str
Identifier of subject.
dwi: str
Path to input Nifti diffusion-weighted volumes.
bvals: str
Path to b-values of diffusion-weighted volumes.
bvecs: str
Path to diffusion-sensitized directions.
bedpostx_dir: str
BedpostX output directory.
subjects_dir: str, default None
Path to the FreeSurfer subjects directory. Required if the
environment variable $SUBJECTS_DIR is not set.
do_eddy: bool, default False
Apply FSL eddy-current correction.
do_rotate_bvecs: bool, default True
Rotate bvecs to match eddy-current correction.
do_bbregister: bool, default True
Register diffusion to T1 using bbregister.
do_register_mni:
Register T1 to MNI.
temp_dir: str, default None
Directory to use to store temporary files. By default OS tmp dir.
fsconfig: str, default <pyfreesurfer.DEFAULT_FREESURFER_PATH>
Path to the FreeSurfer configuration file.
Returns
-------
subject_outdir: str
Path to subject's output directory.
"""
# FreeSurfer $SUBJECTS_DIR has to be passed or set as an env variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Check existence of input files/directories
for path in [dwi, bvals, bvecs, bedpostx_dir, fsconfig]:
if not os.path.exists(path):
raise ValueError("File or directory does not exist: %s" % path)
# Load bvecs and number of b0 volumes
_, bvecs_array, _, nb_b0s = read_bvals_bvecs(bvals_path=bvals,
bvecs_path=bvecs,
min_bval=200.)
# Create directory <outdir>/<subject_id>
subject_outdir = os.path.join(outdir, subject_id)
if not os.path.isdir(subject_outdir):
os.makedirs(subject_outdir)
# Create directory for temporary files
temp_dir = tempfile.mkdtemp(prefix="trac-all_", dir=temp_dir)
# The bvecs can be stored as a 3 x N or N x 3 matrix (N directions).
# FreeSurfer requires the 2nd convention (one row per direction).
# read_bvals_bvecs() always loads the bvecs file as a N x 3 numpy array,
# save this numpy array in a temporary directory and use it as the
# bvecs file to be sure to be in the right convention.
bvecs_Nx3 = os.path.join(temp_dir, "bvecs_Nx3")
numpy.savetxt(bvecs_Nx3, bvecs_array)
# Create configuration file
config_str = CONFIG_TEMPLATE.format(subjects_dir=subjects_dir,
subjlist=subject_id,
dtroot=outdir,
dcmlist=dwi,
bveclist=bvecs_Nx3,
bvallist=bvals,
doeddy=do_eddy,
dorotbvecs=do_rotate_bvecs,
doregbbr=do_bbregister,
doregmni=do_register_mni)
path_config = os.path.join(temp_dir, "trac-all.dmrirc")
with open(path_config, "w") as f:
f.write(config_str)
# Run Tracula preparation
cmd_prep = ["trac-all", "-prep", "-c", path_config]
FSWrapper(cmd_prep,
shfile=fsconfig,
subjects_dir=subjects_dir,
add_fsl_env=True)()
# Tracula requires the BedpostX files to be stored in
# <outdir>/<subject_id>/dmri.bedpostX
create_bedpostx_organization(subject_id=subject_id,
subjects_dir=outdir,
bedpostx_dir=bedpostx_dir)
# Tracula pathways tractography
cmd_path = ["trac-all", "-path", "-c", path_config]
FSWrapper(cmd_path,
shfile=fsconfig,
subjects_dir=subjects_dir,
add_fsl_env=True)()
# Clean tmp dir
shutil.rmtree(temp_dir)
return subject_outdir
def data_import_and_qspace_sampling(
outdir,
subject_id,
dwis,
bvals,
bvecs,
manufacturer,
flipX=False,
flipY=False,
flipZ=False,
invertX=True,
invertY=False,
invertZ=False,
b0_magnitude=None,
b0_phase=None,
phase_axis="y",
slice_axis="z",
path_connectomist=DEFAULT_CONNECTOMIST_PATH):
""" Wrapper to Connectomist's 'DWI & Q-space' tab.
Parameters
----------
outdir: str
path to Connectomist's output directory.
subject_id: str
the subject code in study.
dwis: list of str
path to Nifti diffusion-weighted datasets.
bvals: list of str
path to .bval files associated to the Nifti.
bvecs: list of str
path to .bvec files associated to the Nifti.
manufacturer: str
name of the manufacturer (e.g. "Siemens", "GE", "Philips" or "Bruker").
flipX, flipY, flipZ: bool (optional, default False)
if True invert the x, y or z-axis of data.
invertX: bool (optional, default True)
if True invert x-axis of diffusion model.
invertY, invertZ: bool (optional, default False)
if True invert y or z-axis of diffusion model.
b0_magnitude: str (optional, default None)
path to the magnitude fieldmap (if fieldmap-based correction of
susceptibility distortions is to be used).
b0_phase: str (optional, default None)
path to phase fieldmap (if fieldmap-based correction of susceptibility
distortions is to be used).
phase_axis: str (optional, default 'y')
the acquistion phase axis 'x', 'y' or 'z'.
slice_axis: str (optional, default 'z')
the acquistion slice axis 'x', 'y' or 'z'.
path_connectomist: str (optional)
path to the Connectomist executable.
Returns
-------
outdir: str
path to Connectomist's output directory.
"""
# Check input parameters
for axis in (phase_axis, slice_axis):
if axis not in AXIS:
raise ValueError("Invalid axis '{0}'.".format(axis))
if len(set(map(len, (dwis, bvecs, bvals)))) != 1:
raise ValueError("The DWIs, BVECs and BVALs must have the same number "
"of elements.")
# Create the Connectomist's import data directory
dwis, bvals, bvecs, b0_magnitude, b0_phase = gather_and_format_input_files(
outdir, dwis, bvals, bvecs, b0_magnitude, b0_phase)
# Dict with all parameters for connectomist
algorithm = "DWI-Data-Import-And-QSpace-Sampling"
parameters_dict = {
# Parameters are ordered as they appear in connectomist's GUI
# ---------------------------------------------------------------------
# Field: "Diffusion weighted-images"
"fileNameDwi": ";".join(dwis), # "DW data"
"sliceAxis": AXIS[slice_axis], # "Slice axis", default "Z-axis"
"phaseAxis": AXIS[phase_axis], # "Phase axis", default "Y-axis"
"manufacturer": None,
# Subfield: "Advanced parameters"
"flipAlongX": 2 if flipX else 0, # "Flip data along x"
"flipAlongY": 2 if flipY else 0,
"flipAlongZ": 2 if flipZ else 0,
"numberOfDiscarded": 0, # "#discarded images at beginning"
"numberOfT2": None, # "#T2"
"numberOfRepetitions": 1, # "#repetitions"
# ---------------------------------------------------------------------
# Field: "Rotation of field of view", default is identity matrix
"qSpaceTransform_xx": 1.0,
"qSpaceTransform_xy": 0.0,
"qSpaceTransform_xz": 0.0,
"qSpaceTransform_yx": 0.0,
"qSpaceTransform_yy": 1.0,
"qSpaceTransform_yz": 0.0,
"qSpaceTransform_zx": 0.0,
"qSpaceTransform_zy": 0.0,
"qSpaceTransform_zz": 1.0,
# ---------------------------------------------------------------------
# Field: "Q-space sampling"
"qSpaceSamplingType": 4, # default "spherical single-shell custom"
"qSpaceChoice5BValueFileNames": ";".join(bvals),
"qSpaceChoice5BValueThreshold": 50,
"qSpaceChoice5OrientationFileNames": ";".join(bvecs),
# Apparently Connectomist uses 2 as True, and 0 as False.
"invertXAxis": 2 if invertX else 0,
"invertYAxis": 2 if invertY else 0,
"invertZAxis": 2 if invertZ else 0,
# In this field but not used/handled parameters
"qSpaceChoice1MaximumBValue": 1000, # case Cartesian
"qSpaceChoice2BValue": 1000,
"qSpaceChoice3BValue": 1000,
"qSpaceChoice4BValue": 1000,
"qSpaceChoice6BValues": "",
"qSpaceChoice7BValues": "",
"qSpaceChoice8BValues": "",
"qSpaceChoice9BValues": "",
"qSpaceChoice10BValues": "",
"qSpaceChoice11BValues": "",
"qSpaceChoice1NumberOfSteps": 11,
"qSpaceChoice2NumberOfOrientations": 6,
"qSpaceChoice3NumberOfOrientations": 6,
"qSpaceChoice4NumberOfOrientations": 6,
"qSpaceChoice6NumberOfOrientations": 6,
"qSpaceChoice7NumberOfOrientations": 6,
"qSpaceChoice8NumberOfOrientations": 6,
"qSpaceChoice10NumberOfOrientations": "",
"qSpaceChoice11NumberOfOrientations": "",
"qSpaceChoice13OrientationFileNames": "",
# ---------------------------------------------------------------------
# Field: micro structure config"
"gradientCharacteristicsFileNames": "",
# ---------------------------------------------------------------------
# Field: "Work directory"
"outputWorkDirectory": outdir,
# ---------------------------------------------------------------------
# unknown parameter
"_subjectName": subject_id,
}
# Map the manufacturer name with Connectomist convention
if manufacturer not in MANUFACTURERS:
raise ConnectomistBadManufacturerNameError(manufacturer)
parameters_dict["manufacturer"] = MANUFACTURERS[manufacturer]
# Read bvals and bvecs
bvalues, bvectors, nb_shells, nb_nodiff = read_bvals_bvecs(bvals, bvecs)
# Update Connectomist step description
parameters_dict["numberOfT2"] = nb_nodiff
if nb_shells == 1:
# Spherical single-shell custom
parameters_dict["qSpaceSamplingType"] = 4
else:
warnings.warn(
"'{0}' shell model(s) not handled yet.".format(nb_shells))
# Arbitrary shell
parameters_dict["qSpaceSamplingType"] = 12
parameters_dict["qSpaceChoice13BValueFileNames"] = ";".join(bvals)
parameters_dict["qSpaceChoice13BValueThreshold"] = 50.0
parameters_dict["qSpaceChoice13OrientationFileNames"] = ";".join(bvecs)
# Call with Connectomist
process = ConnectomistWrapper(path_connectomist)
parameter_file = ConnectomistWrapper.create_parameter_file(
algorithm, parameters_dict, outdir)
process(algorithm, parameter_file, outdir)
# When there are multiple volume or when there are multiple t2 (nodif)
# volumes, Connectomist merges them
# rewrite bvec, bval file accordingly (remove extra T2 values)
if nb_nodiff > 1:
bval = os.path.join(outdir, "dwi.bval")
dw_indexes = np.where(bvalues >= 100)[0]
new_bvals = np.concatenate(([0], bvalues[dw_indexes]))
np.savetxt(bval, new_bvals)
bvec = os.path.join(outdir, "dwi.bvec")
new_bvecs = np.concatenate(
([[0], [0], [0]], bvectors.T[:, dw_indexes]), axis=1)
np.savetxt(bvec, new_bvecs)
return outdir
def trac_all_longitudinal(outdir, subject_template_id, subject_timepoint_ids,
dwis, bvalss, bvecss, bedpostx_dirs,
subjects_dir=None, do_eddy=False,
do_rotate_bvecs=True, do_bbregister=True,
do_register_mni=True, temp_dir=None,
fsconfig=DEFAULT_FREESURFER_PATH):
"""
Parameters
----------
outdir: str
Root directory where to create the subject's output directories.
Created if not existing.
subject_template_id: str
Identifier of the subject template.
subject_timepoint_ids: str
Identifiers of the subject for all the timepoints.
dwis: list of str
Paths to Nifti diffusion series. In the order corresponding to
<subject_timepoint_ids>.
bvalss: list of str
Paths to b-values of diffusion series. In the order corresponding to
<dwis>.
bvecss: list of str
Paths to diffusion-sensitized directions of diffusion series. In the
order corresponding to <dwis>.
bedpostx_dirs: list of str
BedpostX output directories. In the order corresponding to <dwis>.
subjects_dir: str, default None
Path to the FreeSurfer longitudinal subjects directory. Required if
the environment variable $SUBJECTS_DIR is not set.
do_eddy: bool, default False
Apply FSL eddy-current correction.
do_rotate_bvecs: bool, default True
Rotate bvecs to match eddy-current correction.
do_bbregister: bool, default True
Register diffusion to T1 using bbregister.
do_register_mni:
Register T1 to MNI.
temp_dir: str, default None
Set the root temporary directory.
fsconfig: str, default <pyfreesurfer.DEFAULT_FREESURFER_PATH>
Path to the FreeSurfer configuration file.
Returns
-------
subject_long_outdirs: list of str
Path to longitudinal subject's output directories.
"""
# Check input arguments
# Check that the user has passed non-empty lists of the same length
list_args = [subject_timepoint_ids, dwis, bvalss, bvecss, bedpostx_dirs]
are_all_lists = all(map(lambda x: isinstance(x, list), list_args))
all_same_size = len(set(map(len, list_args))) == 1
non_empty = len(subject_timepoint_ids) > 1
if not (are_all_lists & all_same_size & non_empty):
raise ValueError("'subject_timepoint_ids', 'dwis', 'bvals', 'bvecs' "
"and 'bedpostx_dirs' must be lists of IDs/paths.")
# FreeSurfer $SUBJECTS_DIR has to be passed or set as an env variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Check existence of input files/directories
input_paths = dwis + bvalss + bvecss + bedpostx_dirs + [fsconfig]
for path in input_paths:
if not os.path.exists(path):
raise ValueError("File or directory does not exist: %s" % path)
# Create directory for temporary files
temp_dir = tempfile.mkdtemp(prefix="trac-all_longitudinal_", dir=temp_dir)
# The bvecs can be stored as a 3 x N or N x 3 matrix (N directions).
# FreeSurfer requires the 2nd convention (one row per direction).
# read_bvals_bvecs() always loads the bvecs file as a N x 3 numpy array,
# save this numpy array in a temporary directory and use it as the
# bvecs file to be sure to be in the right convention.
bvecss_Nx3 = []
for tp_sid, bvals, bvecs in zip(subject_timepoint_ids, bvalss, bvecss):
_, bvecs_array, _, _ = read_bvals_bvecs(bvals_path=bvals,
bvecs_path=bvecs,
min_bval=200.)
bvecs_Nx3 = os.path.join(temp_dir, "bvecs_Nx3_%s" % tp_sid)
numpy.savetxt(bvecs_Nx3, bvecs_array)
bvecss_Nx3.append(bvecs_Nx3)
# Create configuration file
str_subjlist = " ".join(subject_timepoint_ids)
str_baselist = " ".join([subject_template_id] * len(subject_timepoint_ids))
str_dwis = " ".join(dwis)
str_bveclist = " ".join(bvecss_Nx3)
str_bvallist = " ".join(bvalss)
config_str = LONG_CONFIG_TEMPLATE.format(subjects_dir=subjects_dir,
subjlist=str_subjlist,
baselist=str_baselist,
dtroot=outdir,
dcmlist=str_dwis,
bveclist=str_bveclist,
bvallist=str_bvallist,
doeddy=do_eddy,
dorotbvecs=do_rotate_bvecs,
doregbbr=do_bbregister,
doregmni=do_register_mni)
path_config = os.path.join(temp_dir, "trac-all.long.dmrirc")
with open(path_config, "w") as f:
f.write(config_str)
# For each timepoint of subject:
subject_long_outdirs = []
for tp_sid, bedpostx_dir in zip(subject_timepoint_ids, bedpostx_dirs):
# Create <outdir>/<tp sid>.long.<template id> if not existing
long_sid = "%s.long.%s" % (tp_sid, subject_template_id)
long_outdir = os.path.join(outdir, long_sid)
if not os.path.isdir(long_outdir):
os.makedirs(long_outdir)
subject_long_outdirs.append(long_outdir)
# Tracula requires the bedpostX files to be stored in
# <outdir>/<tp sid>.long.<template id>/dmri.bedpostX
create_bedpostx_organization(subject_id=long_sid, subjects_dir=outdir,
bedpostx_dir=bedpostx_dir)
# Run Tracula preparation
cmd_prep = ["trac-all", "-prep", "-c", path_config]
FSWrapper(cmd_prep, shfile=fsconfig, subjects_dir=subjects_dir,
add_fsl_env=True)()
# Tracula pathways tractography
cmd_path = ["trac-all", "-path", "-c", path_config]
FSWrapper(cmd_path, shfile=fsconfig, subjects_dir=subjects_dir,
add_fsl_env=True)()
# Clean tmp dir
shutil.rmtree(temp_dir)
return subject_long_outdirs
def trac_all(outdir, subject_id, dwi, bvals, bvecs, bedpostx_dir,
subjects_dir=None, do_eddy=False, do_rotate_bvecs=True,
do_bbregister=True, do_register_mni=True, temp_dir=None,
fsconfig=DEFAULT_FREESURFER_PATH):
""" Pathway reconstruction.
Parameters
----------
outdir: str
Root directory where to create the subject's output directory.
Created if not existing.
subject_id: str
Identifier of subject.
dwi: str
Path to input Nifti diffusion-weighted volumes.
bvals: str
Path to b-values of diffusion-weighted volumes.
bvecs: str
Path to diffusion-sensitized directions.
bedpostx_dir: str
BedpostX output directory.
subjects_dir: str, default None
Path to the FreeSurfer subjects directory. Required if the
environment variable $SUBJECTS_DIR is not set.
do_eddy: bool, default False
Apply FSL eddy-current correction.
do_rotate_bvecs: bool, default True
Rotate bvecs to match eddy-current correction.
do_bbregister: bool, default True
Register diffusion to T1 using bbregister.
do_register_mni:
Register T1 to MNI.
temp_dir: str, default None
Directory to use to store temporary files. By default OS tmp dir.
fsconfig: str, default <pyfreesurfer.DEFAULT_FREESURFER_PATH>
Path to the FreeSurfer configuration file.
Returns
-------
subject_outdir: str
Path to subject's output directory.
"""
# FreeSurfer $SUBJECTS_DIR has to be passed or set as an env variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Check existence of input files/directories
for path in [dwi, bvals, bvecs, bedpostx_dir, fsconfig]:
if not os.path.exists(path):
raise ValueError("File or directory does not exist: %s" % path)
# Load bvecs and number of b0 volumes
_, bvecs_array, _, nb_b0s = read_bvals_bvecs(bvals_path=bvals,
bvecs_path=bvecs,
min_bval=200.)
# Create directory <outdir>/<subject_id>
subject_outdir = os.path.join(outdir, subject_id)
if not os.path.isdir(subject_outdir):
os.makedirs(subject_outdir)
# Create directory for temporary files
temp_dir = tempfile.mkdtemp(prefix="trac-all_", dir=temp_dir)
# The bvecs can be stored as a 3 x N or N x 3 matrix (N directions).
# FreeSurfer requires the 2nd convention (one row per direction).
# read_bvals_bvecs() always loads the bvecs file as a N x 3 numpy array,
# save this numpy array in a temporary directory and use it as the
# bvecs file to be sure to be in the right convention.
bvecs_Nx3 = os.path.join(temp_dir, "bvecs_Nx3")
numpy.savetxt(bvecs_Nx3, bvecs_array)
# Create configuration file
config_str = CONFIG_TEMPLATE.format(subjects_dir=subjects_dir,
subjlist=subject_id,
dtroot=outdir,
dcmlist=dwi,
bveclist=bvecs_Nx3,
bvallist=bvals,
doeddy=do_eddy,
dorotbvecs=do_rotate_bvecs,
doregbbr=do_bbregister,
doregmni=do_register_mni)
path_config = os.path.join(temp_dir, "trac-all.dmrirc")
with open(path_config, "w") as f:
f.write(config_str)
# Run Tracula preparation
cmd_prep = ["trac-all", "-prep", "-c", path_config]
FSWrapper(cmd_prep, shfile=fsconfig, subjects_dir=subjects_dir,
add_fsl_env=True)()
# Tracula requires the BedpostX files to be stored in
# <outdir>/<subject_id>/dmri.bedpostX
create_bedpostx_organization(subject_id=subject_id, subjects_dir=outdir,
bedpostx_dir=bedpostx_dir)
# Tracula pathways tractography
cmd_path = ["trac-all", "-path", "-c", path_config]
FSWrapper(cmd_path, shfile=fsconfig, subjects_dir=subjects_dir,
add_fsl_env=True)()
# Clean tmp dir
shutil.rmtree(temp_dir)
return subject_outdir
