def complete_preproc_wo_fieldmap(outdir,
dwi,
bval,
bvec,
subject_id,
fs_subjects_dir = None,
invertX = True,
invertY = False,
invertZ = False,
delete_steps = False):
"""
Function that runs all preprocessing steps using Connectomist but
with BrainSuite for the correction of susceptibility distortions.
Parameters
----------
outdir: Str, path to folder where all the preprocessing will be done.
dwi Str, path to input Nifti DW data.
bval: Str, path to Nifti's associated .bval file.
bvec: Str, path to Nifti's associated .bval file.
subject_id: Str, subject identifier used in Freesurfer.
fs_subjects_dir: If the Freesurfer $SUBJECTS_DIR environment variable is
not set, or to bypass it, pass the path.
invertX: Bool, if True invert x-axis in diffusion model.
invertY: Bool, same as invertX but for y-axis.
invertZ: Bool, same as invertX but for z-axis.
delete_steps: Bool, if True remove all intermediate files and
directories at the end of preprocessing, to keep only
selected files:
preprocessed Nifti + bval + bvec + outliers.py + nodif_brain.nii.gz
Returns
-------
outdir: Directory with the preprocessed files.
<unit>
<output name="preproc_dwi" type="File" />
<output name="preproc_bval" type="File" />
<output name="preproc_bvec" type="File" />
<input name="outdir" type="Directory" />
<input name="dwi" type="File" />
<input name="bval" type="File" />
<input name="bvec" type="File" />
<input name="subject_id" type="Str" />
<input name="fs_subjects_dir" type="Str" />
<input name="invertX" type="Bool" />
<input name="invertY" type="Bool" />
<input name="invertZ" type="Bool" />
<input name="delete_steps" type="Bool" />
</unit>
"""
### Step 0 - Initialization
# If Freesurfer SUBJECTS_DIR is not passed, it should be set as environment variable
if fs_subjects_dir is None:
if "SUBJECTS_DIR" in os.environ:
fs_subjects_dir = os.environ["SUBJECTS_DIR"]
else:
raise ValueError("Missing <SUBJECTS_DIR>: set the $SUBJECTS_DIR "
"environment variable for Freesurfer or pass it "
"as an argument.")
# Raise an Exception if BrainsuiteCheck is not installed
check_brainsuite_installation()
# Create the preprocessing output directory if not existing
if not os.path.isdir(outdir):
os.makedirs(outdir)
### Step 1 - Import files to Connectomist and choose q-space model
raw_dwi_dir = os.path.join(outdir, "01-Import_and_qspace_model")
dwi_data_import_and_qspace_sampling(raw_dwi_dir,
dwi = dwi,
bval = bval,
bvec = bvec,
manufacturer = "Siemens", # unused but required
invertX = invertX,
invertY = invertY,
invertZ = invertZ,
subject_id = subject_id)
### Step 2 --- Create a brain mask
rough_mask_dir = os.path.join(outdir, "02-Rough_mask")
dwi_rough_mask_extraction(rough_mask_dir, raw_dwi_dir)
### Step 3 - Detect and correct outlying diffusion slices
outliers_dir = os.path.join(outdir, "03-Outliers")
dwi_outlier_detection(outliers_dir, raw_dwi_dir, rough_mask_dir)
# Export outliers.py
path_outliers_py = os.path.join(outliers_dir, "outliers.py")
shutil.copy(path_outliers_py, outdir)
### Step 4 - Eddy current and motion correction
eddy_motion_dir = os.path.join(outdir, "04-Eddy_current_and_motion")
dwi_eddy_current_and_motion_correction(eddy_motion_dir,
raw_dwi_dir,
rough_mask_dir,
outliers_dir)
### Step 5 - Convert Connectomist result to Nifti with bval/bvec
dwi, bval, bvec = export_eddy_motion_results_to_nifti(eddy_motion_dir,
filename = "dwi_ecc")
### Step 6 - Susceptibility correction using BrainSuite
brainsuite_dir = os.path.join(outdir, "05-Suceptibility_BrainSuite")
if not os.path.isdir(brainsuite_dir):
os.mkdir(brainsuite_dir)
dwi_wo_susceptibility, bval, bvec = \
brainsuite_susceptibility_correction(outdir = brainsuite_dir,
dwi = dwi,
bval = bval,
bvec = bvec,
subject_id = subject_id,
fs_subjects_dir = fs_subjects_dir,
qc_dir = outdir)
### Step 7 - move corrected diffusion to outdir
dwi_preproc = os.path.join(outdir, "dwi.nii.gz")
bval_preproc = os.path.join(outdir, "dwi.bval")
bvec_preproc = os.path.join(outdir, "dwi.bvec")
shutil.copyfile(dwi_wo_susceptibility, dwi_preproc)
shutil.copyfile(bval, bval_preproc)
shutil.copyfile(bvec, bvec_preproc)
### Step 8 - Create a T2 brain mask of preprocessed DWI data
bet2_prefix = os.path.join(outdir, "nodif_brain")
nodif_brain = os.path.join(bet2_prefix + ".nii.gz")
nodif_brain_mask = os.path.join(bet2_prefix + "_mask.nii.gz")
bet2(dwi, bet2_prefix, f=0.25, m=True)
### Step 9 - clean intermediate directories if requested
if delete_steps:
intermediate_directories = [raw_dwi_dir,
rough_mask_dir,
outliers_dir,
eddy_motion_dir,
brainsuite_dir]
for directory in intermediate_directories:
shutil.rmtree(directory)
return outdir, dwi_preproc, bval_preproc, bvec_preproc, nodif_brain, nodif_brain_mask
def complete_preproc_wo_fieldmap(outdir,
dwi,
bval,
bvec,
phase_enc_dir,
subject_id,
subjects_dir=None,
invertX=True,
invertY=False,
invertZ=False,
delete_steps=False):
"""
Function that runs all preprocessing steps using Connectomist but
with BrainSuite for the correction of susceptibility distortions.
Parameters
----------
outdir: str
Path to directory where all the preprocessing will be done.
dwi: str
path to input diffusion-weighted Nifti data.
bval: str
Path to the Nifti's associated .bval file.
bvec: str
Ppath to Nifti's associated .bval file.
phase_enc_dir: str
In plane phase encoding direction, "y", "y-", "x" or "x-".
subject_id: str
Subject identifier used in Freesurfer.
subjects_dir: Str, default None
If the Freesurfer $SUBJECTS_DIR environment variable is not set, or to
bypass it, pass the path.
invertX: bool, default True
If True invert x-axis in diffusion model.
invertY: bool, default False
Same as invertX but for y-axis.
invertZ: bool, default False
Same as invertX but for z-axis.
delete_steps: bool
If True remove all intermediate files and directories at the end of
preprocessing, to keep only selected files:
- preprocessed Nifti + bval + bvec + outliers.py + nodif_brain*.nii.gz
Returns
-------
outdir: str,
Path to directory with the preprocessed files.
<unit>
<output name="preproc_dwi" type="File" />
<output name="preproc_bval" type="File" />
<output name="preproc_bvec" type="File" />
<input name="outdir" type="Directory" />
<input name="dwi" type="File" />
<input name="bval" type="File" />
<input name="bvec" type="File" />
<input name="phase_enc_dir" type="Str" />
<input name="subject_id" type="Str" />
<input name="subjects_dir" type="Str" />
<input name="invertX" type="Bool" />
<input name="invertY" type="Bool" />
<input name="invertZ" type="Bool" />
<input name="delete_steps" type="Bool" />
</unit>
"""
# Step 0 - Initialization
# Freesurfer 'subjects_dir' has to be passed or set as environment variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Raise an Exception if BrainsuiteCheck is not installed
check_brainsuite_installation()
# Create the preprocessing output directory if not existing
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Step 1 - Import files to Connectomist and choose q-space model
raw_dwi_dir = os.path.join(outdir, "01-Import_and_qspace_model")
# The manufacturer option is unused (use brainsuite for susceptibility
# correction) but required
dwi_data_import_and_qspace_sampling(raw_dwi_dir,
dwi=dwi,
bval=bval,
bvec=bvec,
manufacturer="Siemens",
invertX=invertX,
invertY=invertY,
invertZ=invertZ,
subject_id=subject_id)
# Step 2 - Create a brain mask
rough_mask_dir = os.path.join(outdir, "02-Rough_mask")
dwi_rough_mask_extraction(rough_mask_dir, raw_dwi_dir)
# Step 3 - Detect and correct outlying diffusion slices
outliers_dir = os.path.join(outdir, "03-Outliers")
dwi_outlier_detection(outliers_dir, raw_dwi_dir, rough_mask_dir)
# Export outliers.py
path_outliers_py = os.path.join(outliers_dir, "outliers.py")
shutil.copy(path_outliers_py, outdir)
# Step 4 - Eddy current and motion correction
eddy_motion_dir = os.path.join(outdir, "04-Eddy_current_and_motion")
dwi_eddy_current_and_motion_correction(eddy_motion_dir, raw_dwi_dir,
rough_mask_dir, outliers_dir)
# Step 5 - Convert Connectomist result to Nifti with bval/bvec
dwi, bval, bvec = export_eddy_motion_results_to_nifti(eddy_motion_dir,
filename="dwi_ecc")
# Step 6 - Susceptibility correction using BrainSuite
brainsuite_dir = os.path.join(outdir, "05-Suceptibility_BrainSuite")
if not os.path.isdir(brainsuite_dir):
os.mkdir(brainsuite_dir)
dwi_wo_susceptibility, bval, bvec = \
brainsuite_susceptibility_correction(outdir=brainsuite_dir,
dwi=dwi,
bval=bval,
bvec=bvec,
phase_enc_dir=phase_enc_dir,
subject_id=subject_id,
subjects_dir=subjects_dir,
qc_dir=outdir)
# Step 7 - move corrected diffusion to outdir
dwi_preproc = os.path.join(outdir, "dwi.nii.gz")
bval_preproc = os.path.join(outdir, "dwi.bval")
bvec_preproc = os.path.join(outdir, "dwi.bvec")
shutil.copyfile(dwi_wo_susceptibility, dwi_preproc)
shutil.copyfile(bval, bval_preproc)
shutil.copyfile(bvec, bvec_preproc)
# Step 8 - Create a T2 brain mask of preprocessed DWI data
bet2_prefix = os.path.join(outdir, "nodif_brain")
nodif_brain = os.path.join(bet2_prefix + ".nii.gz")
nodif_brain_mask = os.path.join(bet2_prefix + "_mask.nii.gz")
bet2(dwi, bet2_prefix, f=0.25, m=True)
# Step 9 - clean intermediate directories if requested
if delete_steps:
intermediate_directories = [
raw_dwi_dir, rough_mask_dir, outliers_dir, eddy_motion_dir,
brainsuite_dir
]
for directory in intermediate_directories:
shutil.rmtree(directory)
return (outdir, dwi_preproc, bval_preproc, bvec_preproc, nodif_brain,
nodif_brain_mask)
def complete_preproc_wo_fieldmap(outdir,
dwi,
bval,
bvec,
phase_enc_dir,
subject_id,
subjects_dir=None,
invertX=True,
invertY=False,
invertZ=False,
delete_steps=False):
"""
Function that runs all preprocessing steps using Connectomist but
with BrainSuite for the correction of susceptibility distortions.
Parameters
----------
outdir: str
Path to directory where all the preprocessing will be done.
dwi: str
path to input diffusion-weighted Nifti data.
bval: str
Path to the Nifti's associated .bval file.
bvec: str
Ppath to Nifti's associated .bval file.
phase_enc_dir: str
In plane phase encoding direction, "y", "y-", "x" or "x-".
subject_id: str
Subject identifier used in Freesurfer.
subjects_dir: Str, default None
If the Freesurfer $SUBJECTS_DIR environment variable is not set, or to
bypass it, pass the path.
invertX: bool, default True
If True invert x-axis in diffusion model.
invertY: bool, default False
Same as invertX but for y-axis.
invertZ: bool, default False
Same as invertX but for z-axis.
delete_steps: bool
If True remove all intermediate files and directories at the end of
preprocessing, to keep only selected files:
- preprocessed Nifti + bval + bvec + outliers.py + nodif_brain*.nii.gz
Returns
-------
outdir: str,
Path to directory with the preprocessed files.
<unit>
<output name="preproc_dwi" type="File" />
<output name="preproc_bval" type="File" />
<output name="preproc_bvec" type="File" />
<input name="outdir" type="Directory" />
<input name="dwi" type="File" />
<input name="bval" type="File" />
<input name="bvec" type="File" />
<input name="phase_enc_dir" type="Str" />
<input name="subject_id" type="Str" />
<input name="subjects_dir" type="Str" />
<input name="invertX" type="Bool" />
<input name="invertY" type="Bool" />
<input name="invertZ" type="Bool" />
<input name="delete_steps" type="Bool" />
</unit>
"""
# Step 0 - Initialization
# Freesurfer 'subjects_dir' has to be passed or set as environment variable
subjects_dir = get_or_check_freesurfer_subjects_dir(subjects_dir)
# Raise an Exception if BrainsuiteCheck is not installed
check_brainsuite_installation()
# Create the preprocessing output directory if not existing
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Step 1 - Import files to Connectomist and choose q-space model
raw_dwi_dir = os.path.join(outdir, "01-Import_and_qspace_model")
# The manufacturer option is unused (use brainsuite for susceptibility
# correction) but required
dwi_data_import_and_qspace_sampling(raw_dwi_dir,
dwi=dwi,
bval=bval,
bvec=bvec,
manufacturer="Siemens",
invertX=invertX,
invertY=invertY,
invertZ=invertZ,
subject_id=subject_id)
# Step 2 - Create a brain mask
rough_mask_dir = os.path.join(outdir, "02-Rough_mask")
dwi_rough_mask_extraction(rough_mask_dir, raw_dwi_dir)
# Step 3 - Detect and correct outlying diffusion slices
outliers_dir = os.path.join(outdir, "03-Outliers")
dwi_outlier_detection(outliers_dir, raw_dwi_dir, rough_mask_dir)
# Export outliers.py
path_outliers_py = os.path.join(outliers_dir, "outliers.py")
shutil.copy(path_outliers_py, outdir)
# Step 4 - Eddy current and motion correction
eddy_motion_dir = os.path.join(outdir, "04-Eddy_current_and_motion")
dwi_eddy_current_and_motion_correction(eddy_motion_dir,
raw_dwi_dir,
rough_mask_dir,
outliers_dir)
# Step 5 - Convert Connectomist result to Nifti with bval/bvec
dwi, bval, bvec = export_eddy_motion_results_to_nifti(eddy_motion_dir,
filename="dwi_ecc")
# Step 6 - Susceptibility correction using BrainSuite
brainsuite_dir = os.path.join(outdir, "05-Suceptibility_BrainSuite")
if not os.path.isdir(brainsuite_dir):
os.mkdir(brainsuite_dir)
dwi_wo_susceptibility, bval, bvec = \
brainsuite_susceptibility_correction(outdir=brainsuite_dir,
dwi=dwi,
bval=bval,
bvec=bvec,
phase_enc_dir=phase_enc_dir,
subject_id=subject_id,
subjects_dir=subjects_dir,
qc_dir=outdir)
# Step 7 - move corrected diffusion to outdir
dwi_preproc = os.path.join(outdir, "dwi.nii.gz")
bval_preproc = os.path.join(outdir, "dwi.bval")
bvec_preproc = os.path.join(outdir, "dwi.bvec")
shutil.copyfile(dwi_wo_susceptibility, dwi_preproc)
shutil.copyfile(bval, bval_preproc)
shutil.copyfile(bvec, bvec_preproc)
# Step 8 - Create a T2 brain mask of preprocessed DWI data
bet2_prefix = os.path.join(outdir, "nodif_brain")
nodif_brain = os.path.join(bet2_prefix + ".nii.gz")
nodif_brain_mask = os.path.join(bet2_prefix + "_mask.nii.gz")
bet2(dwi, bet2_prefix, f=0.25, m=True)
# Step 9 - clean intermediate directories if requested
if delete_steps:
intermediate_directories = [raw_dwi_dir,
rough_mask_dir,
outliers_dir,
eddy_motion_dir,
brainsuite_dir]
for directory in intermediate_directories:
shutil.rmtree(directory)
return (outdir, dwi_preproc, bval_preproc, bvec_preproc, nodif_brain,
nodif_brain_mask)
