def mask_image(self,folder,in_path,out_path):
"""
This function for creating mask_image.
It is dependent on the class name: func.
folder : read the string, new folder's name.
in_path : read the list, element: string, directory address of data.
out_path : read the string, name of the new data.
For complete details, see the BET() documentation.
<https://nipype.readthedocs.io/en/latest/interfaces.html>
"""
print('#################_Mask_image_started_#######################')
preprocessing = time.time()
# Check and if not make the directory
if not os.path.isdir(folder):
func.newfolder(folder)
# Create, and save the data : anatomy and whole brain mask image
for i in range(len(in_path)):
output = folder+'/'+out_path[i]
print(in_path[i], "mask image started")
skullstrip = BET(in_file=in_path[i],
out_file=output,
mask=True)
skullstrip.run()
print(output,"mask image completed")
print("computation_time :","%.2fs" %(time.time() - preprocessing))
print('#################_Mask_image_completed_#####################')
def mask_CTBrain(CT_dir, T1_dir, frac):
# nav to dir with T1 images
os.chdir(T1_dir)
# run BET to extract brain from T1
bet = BET()
bet.inputs.in_file = T1_dir + '/T1.nii'
bet.inputs.frac = frac
bet.inputs.robust = True
bet.inputs.mask = True
print "::: Extracting Brain mask from T1 using BET :::"
bet.run()
# use flrit to correg CT to T1
flirt = FLIRT()
flirt.inputs.in_file = CT_dir + '/CT.nii'
flirt.inputs.reference = T1_dir + '/T1.nii'
flirt.inputs.cost_func = 'mutualinfo'
print "::: Estimating corregistration from CT to T1 using FLIRT :::"
flirt.run()
# get inverse of estimated coreg of CT tp T1
print "::: Estimating inverse affine for Brain mask of CT :::"
os.system('convert_xfm -omat inv_CT_flirt.mat -inverse CT_flirt.mat')
os.system('mv inv_CT_flirt.mat %s' % (CT_dir))
# apply inverse affine coreg to get brain mask in CT space
applyxfm = ApplyXfm()
applyxfm.inputs.in_file = T1_dir + '/T1_brain_mask.nii.gz'
applyxfm.inputs.in_matrix_file = CT_dir + '/inv_CT_flirt.mat'
applyxfm.inputs.out_file = CT_dir + '/CT_mask.nii.gz'
applyxfm.inputs.reference = CT_dir + '/CT.nii'
applyxfm.inputs.apply_xfm = True
print "::: Applying inverse affine to Brain mask to get CT mask :::"
applyxfm.run()
# dilate brain mask to make sure all elecs are in final masked img
CT_mask = nib.load(CT_dir + '/CT_mask.nii.gz')
CT_mask_dat = CT_mask.get_data()
kernel = np.ones((5, 5), np.uint8)
print "::: Dilating CT mask :::"
dilated = cv2.dilate(CT_mask_dat, kernel, iterations=1)
hdr = CT_mask.get_header()
affine = CT_mask.get_affine()
N = nib.Nifti1Image(dilated, affine, hdr)
new_fname = CT_dir + '/dilated_CT_mask.nii'
N.to_filename(new_fname)
# apply mask to CT
os.chdir(CT_dir)
print "::: masking CT with dilated brain mask :::"
os.system(
"fslmaths 'CT.nii' -mas 'dilated_CT_mask.nii.gz' 'masked_CT.nii'")
os.system('gunzip masked_CT.nii.gz')
def mask_CTBrain(CT_dir, T1_dir, frac):
# nav to dir with T1 images
os.chdir(T1_dir)
# run BET to extract brain from T1
bet = BET()
bet.inputs.in_file = T1_dir + "/T1.nii"
bet.inputs.frac = frac
bet.inputs.robust = True
bet.inputs.mask = True
print "::: Extracting Brain mask from T1 using BET :::"
bet.run()
# use flrit to correg CT to T1
flirt = FLIRT()
flirt.inputs.in_file = CT_dir + "/CT.nii"
flirt.inputs.reference = T1_dir + "/T1.nii"
flirt.inputs.cost_func = "mutualinfo"
print "::: Estimating corregistration from CT to T1 using FLIRT :::"
flirt.run()
# get inverse of estimated coreg of CT tp T1
print "::: Estimating inverse affine for Brain mask of CT :::"
os.system("convert_xfm -omat inv_CT_flirt.mat -inverse CT_flirt.mat")
os.system("mv inv_CT_flirt.mat %s" % (CT_dir))
# apply inverse affine coreg to get brain mask in CT space
applyxfm = ApplyXfm()
applyxfm.inputs.in_file = T1_dir + "/T1_brain_mask.nii.gz"
applyxfm.inputs.in_matrix_file = CT_dir + "/inv_CT_flirt.mat"
applyxfm.inputs.out_file = CT_dir + "/CT_mask.nii.gz"
applyxfm.inputs.reference = CT_dir + "/CT.nii"
applyxfm.inputs.apply_xfm = True
print "::: Applying inverse affine to Brain mask to get CT mask :::"
applyxfm.run()
# dilate brain mask to make sure all elecs are in final masked img
CT_mask = nib.load(CT_dir + "/CT_mask.nii.gz")
CT_mask_dat = CT_mask.get_data()
kernel = np.ones((5, 5), np.uint8)
print "::: Dilating CT mask :::"
dilated = cv2.dilate(CT_mask_dat, kernel, iterations=1)
hdr = CT_mask.get_header()
affine = CT_mask.get_affine()
N = nib.Nifti1Image(dilated, affine, hdr)
new_fname = CT_dir + "/dilated_CT_mask.nii"
N.to_filename(new_fname)
# apply mask to CT
os.chdir(CT_dir)
print "::: masking CT with dilated brain mask :::"
os.system("fslmaths 'CT.nii' -mas 'dilated_CT_mask.nii.gz' 'masked_CT.nii'")
os.system("gunzip masked_CT.nii.gz")
def bet(record):
import nipype
from nipype.interfaces.fsl import BET
from nipype.utils.filemanip import hash_infile
nipype.config.enable_provenance()
in_file_uri = record['t1_uri']
os.chdir('/tmp')
fname = 'anatomy.nii.gz'
with open(fname, 'wb') as fd:
response = requests.get(in_file_uri, stream=True)
if not response.ok:
response.raise_for_status()
for chunk in response.iter_content(1024):
fd.write(chunk)
# Check if interface has run with this input before
sha = hash_infile(os.path.abspath(fname), crypto=hashlib.sha512)
select = SelectQuery(config=app.config)
res = select.execute_select('E0921842-1EDB-49F8-A4B3-BA51B85AD407')
sha_recs = res[res.sha512.str.contains(sha)]
bet_recs = res[res.interface.str.contains('BET')]
results = dict()
if sha_recs.empty or \
(not sha_recs.empty and bet_recs.empty):
better = BET()
better.inputs.in_file = os.path.abspath(fname)
result = better.run()
prov = result.provenance.rdf().serialize(format='json-ld')
results.update({'prov': prov})
else:
results.update({'prov': res.to_json(orient='records')})
return results
def skullstrip(infile: Path) -> Tuple[Path, Path]:
cmd = BET()
cmd.inputs.in_file = str(infile)
cmd.inputs.out_file = str(infile).replace(SLICETIME_SUFFIX, BET_SUFFIX)
cmd.inputs.output_type = "NIFTI_GZ"
cmd.inputs.functional = True
cmd.inputs.mask = True
results = cmd.run()
return results.outputs.out_file, results.outputs.mask_file
def test_skull_stripping_ids_only():
data_dir = os.path.abspath('.') + '/data/ds114'
pipeline_name = 'test_skullStrippingTransformer_ds114'
IDS = ['01', '02', '03']
sessions = ['test', 'retest']
transformer = SkullStrippingTransformer(
data_dir=data_dir,
pipeline_name=pipeline_name,
search_param=dict(extensions='T1w.nii.gz'),
variant='skullStrippingIDsOnly')
transformer.fit_transform(IDS)
for subject in IDS:
for session in sessions:
in_file = build_T1w_in_file_path(data_dir, subject, session)
out_file = build_T1w_out_file_path(data_dir, pipeline_name,
subject, session,
'skullStrippingIDsOnly',
'neededBrain')
dirname = os.path.dirname(out_file)
if not os.path.exists(dirname):
os.makedirs(dirname)
betfsl = BET(in_file=in_file, out_file=out_file)
betfsl.run()
res_file = build_T1w_out_file_path(data_dir, pipeline_name,
subject, session,
'skullStrippingIDsOnly',
'brain')
result_mat = nib.load(res_file).affine
output_mat = nib.load(out_file).affine
assert np.allclose(result_mat, output_mat)
os.remove(res_file)
os.remove(out_file)
def run_bet(robust: bool = True, skip_existing: bool = True):
scans = get_scans(LOCATION_DICT["raw"])
for scan in scans:
print(f"\nCurrent series: {scan}")
if skip_existing:
print("Checking for existing skull-stripping output...", end="\t")
dest = get_default_destination(scan)
if skip_existing and os.path.isfile(dest):
print(f"\u2714")
continue
print(f"\u2718")
print("Running skull-stripping with BET...", end="\t")
try:
bet = BET(robust=True)
bet.inputs.in_file = scan
bet.inputs.out_file = dest
bet.run()
print(f"\u2714\tDone!")
except Exception as e:
print(f"\u2718")
print(e.args)
break
def functional_per_participant_test():
for i in ["", "_aF", "_cF1", "_cF2", "_pF"]:
template = "~/ni_data/templates/ds_QBI_chr.nii.gz"
participant = "4008"
image_dir = "~/ni_data/ofM.dr/preprocessing/generic_work/_subject_session_{}.ofM{}/_scan_type_7_EPI_CBV/temporal_mean/".format(
participant, i)
try:
for myfile in os.listdir(image_dir):
if myfile.endswith(".nii.gz"):
mimage = os.path.join(image_dir, myfile)
except FileNotFoundError:
pass
else:
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = mimage
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200, 200, 200, 200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = 'n4_{}_ofM{}.nii.gz'.format(
participant, i)
n4_res = n4.run()
functional_cutoff = ImageMaths()
functional_cutoff.inputs.op_string = "-thrP 30"
functional_cutoff.inputs.in_file = n4_res.outputs.output_image
functional_cutoff_res = functional_cutoff.run()
functional_BET = BET()
functional_BET.inputs.mask = True
functional_BET.inputs.frac = 0.5
functional_BET.inputs.in_file = functional_cutoff_res.outputs.out_file
functional_BET_res = functional_BET.run()
registration = ants.Registration()
registration.inputs.fixed_image = template
registration.inputs.output_transform_prefix = "output_"
registration.inputs.transforms = ['Affine', 'SyN']
registration.inputs.transform_parameters = [(0.1, ),
(3.0, 3.0, 5.0)]
registration.inputs.number_of_iterations = [[10000, 10000, 10000],
[100, 100, 100]]
registration.inputs.dimension = 3
registration.inputs.write_composite_transform = True
registration.inputs.collapse_output_transforms = True
registration.inputs.initial_moving_transform_com = True
registration.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
registration.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
registration.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
registration.inputs.sampling_strategy = ['Regular'] * 2 + [[
None, None
]]
registration.inputs.sampling_percentage = [0.3] * 2 + [[
None, None
]]
registration.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
registration.inputs.convergence_window_size = [20] * 2 + [5]
registration.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[
1, 0.5, 0
]]
registration.inputs.sigma_units = ['vox'] * 3
registration.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
registration.inputs.use_estimate_learning_rate_once = [True] * 3
registration.inputs.use_histogram_matching = [False] * 2 + [True]
registration.inputs.winsorize_lower_quantile = 0.005
registration.inputs.winsorize_upper_quantile = 0.995
registration.inputs.args = '--float'
registration.inputs.num_threads = 4
registration.plugin_args = {
'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'
}
registration.inputs.moving_image = functional_BET_res.outputs.out_file
registration.inputs.output_warped_image = '{}_ofM{}.nii.gz'.format(
participant, i)
res = registration.run()
def structural_to_functional_per_participant_test(
subjects_sessions,
template="~/GitHub/mriPipeline/templates/waxholm/new/WHS_SD_masked.nii.gz",
f_file_format="~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_SE_EPI/f_bru2nii/",
s_file_format="~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_T2_TurboRARE/s_bru2nii/",
num_threads=3,
):
template = os.path.expanduser(template)
for subject_session in subjects_sessions:
func_image_dir = os.path.expanduser(
f_file_format.format(**subject_session))
struct_image_dir = os.path.expanduser(
s_file_format.format(**subject_session))
try:
for myfile in os.listdir(func_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
func_image = os.path.join(func_image_dir, myfile)
for myfile in os.listdir(struct_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
struct_image = os.path.join(struct_image_dir, myfile)
except FileNotFoundError:
pass
else:
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200, 200, 200, 200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = '{}_{}_1_biasCorrection_forRegistration.nii.gz'.format(
*subject_session.values())
n4_res = n4.run()
_n4 = ants.N4BiasFieldCorrection()
_n4.inputs.dimension = 3
_n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
_n4.inputs.bspline_fitting_distance = 95
_n4.inputs.shrink_factor = 2
_n4.inputs.n_iterations = [500, 500, 500, 500]
_n4.inputs.convergence_threshold = 1e-14
_n4.inputs.output_image = '{}_{}_1_biasCorrection_forMasking.nii.gz'.format(
*subject_session.values())
_n4_res = _n4.run()
#we do this on a separate bias-corrected image to remove hyperintensities which we have to create in order to prevent brain regions being caught by the negative threshold
struct_cutoff = ImageMaths()
struct_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
struct_cutoff.inputs.in_file = _n4_res.outputs.output_image
struct_cutoff_res = struct_cutoff.run()
struct_BET = BET()
struct_BET.inputs.mask = True
struct_BET.inputs.frac = 0.3
struct_BET.inputs.robust = True
struct_BET.inputs.in_file = struct_cutoff_res.outputs.out_file
struct_BET.inputs.out_file = '{}_{}_2_brainExtraction.nii.gz'.format(
*subject_session.values())
struct_BET_res = struct_BET.run()
# we need/can not apply a fill, because the "holes" if any, will be at the rostral edge (touching it, and thus not counting as holes)
struct_mask = ApplyMask()
struct_mask.inputs.in_file = n4_res.outputs.output_image
struct_mask.inputs.mask_file = struct_BET_res.outputs.mask_file
struct_mask.inputs.out_file = '{}_{}_3_brainMasked.nii.gz'.format(
*subject_session.values())
struct_mask_res = struct_mask.run()
struct_registration = ants.Registration()
struct_registration.inputs.fixed_image = template
struct_registration.inputs.output_transform_prefix = "output_"
struct_registration.inputs.transforms = ['Affine', 'SyN'] ##
struct_registration.inputs.transform_parameters = [(1.0, ),
(1.0, 3.0, 5.0)
] ##
struct_registration.inputs.number_of_iterations = [[
2000, 1000, 500
], [100, 100, 100]] #
struct_registration.inputs.dimension = 3
struct_registration.inputs.write_composite_transform = True
struct_registration.inputs.collapse_output_transforms = True
struct_registration.inputs.initial_moving_transform_com = True
# Tested on Affine transform: CC takes too long; Demons does not tilt, but moves the slices too far caudally; GC tilts too much on
struct_registration.inputs.metric = ['MeanSquares', 'Mattes']
struct_registration.inputs.metric_weight = [1, 1]
struct_registration.inputs.radius_or_number_of_bins = [16, 32] #
struct_registration.inputs.sampling_strategy = ['Random', None]
struct_registration.inputs.sampling_percentage = [0.3, 0.3]
struct_registration.inputs.convergence_threshold = [1.e-11,
1.e-8] #
struct_registration.inputs.convergence_window_size = [20, 20]
struct_registration.inputs.smoothing_sigmas = [[4, 2, 1],
[4, 2, 1]]
struct_registration.inputs.sigma_units = ['vox', 'vox']
struct_registration.inputs.shrink_factors = [[3, 2, 1], [3, 2, 1]]
struct_registration.inputs.use_estimate_learning_rate_once = [
True, True
]
# if the fixed_image is not acquired similarly to the moving_image (e.g. RARE to histological (e.g. AMBMC)) this should be False
struct_registration.inputs.use_histogram_matching = [False, False]
struct_registration.inputs.winsorize_lower_quantile = 0.005
struct_registration.inputs.winsorize_upper_quantile = 0.98
struct_registration.inputs.args = '--float'
struct_registration.inputs.num_threads = num_threads
struct_registration.inputs.moving_image = struct_mask_res.outputs.out_file
struct_registration.inputs.output_warped_image = '{}_{}_4_structuralRegistration.nii.gz'.format(
*subject_session.values())
struct_registration_res = struct_registration.run()
warp = ants.ApplyTransforms()
warp.inputs.reference_image = template
warp.inputs.input_image_type = 3
warp.inputs.interpolation = 'Linear'
warp.inputs.invert_transform_flags = [False]
warp.inputs.terminal_output = 'file'
warp.inputs.output_image = '{}_{}_5_functionalWarp.nii.gz'.format(
*subject_session.values())
warp.num_threads = num_threads
warp.inputs.input_image = func_image
warp.inputs.transforms = struct_registration_res.outputs.composite_transform
warp.run()
def canonical(subjects_participants, regdir, f2s,
template = "~/GitHub/mriPipeline/templates/waxholm/WHS_SD_rat_T2star_v1.01_downsample3.nii.gz",
f_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_SE_EPI/f_bru2nii/",
s_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_T2_TurboRARE/s_bru2nii/",
):
"""Warp a functional image based on the functional-to-structural and the structural-to-template registrations.
Currently this approach is failing because the functiona-to-structural registration pushes the brain stem too far down.
This may be
"""
template = os.path.expanduser(template)
for subject_participant in subjects_participants:
func_image_dir = os.path.expanduser(f_file_format.format(**subject_participant))
struct_image_dir = os.path.expanduser(s_file_format.format(**subject_participant))
try:
for myfile in os.listdir(func_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
func_image = os.path.join(func_image_dir,myfile)
for myfile in os.listdir(struct_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
struct_image = os.path.join(struct_image_dir,myfile)
except FileNotFoundError:
pass
else:
#struct
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200,200,200,200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = '{}/ss_n4_{}_ofM{}.nii.gz'.format(regdir,participant,i)
n4_res = n4.run()
_n4 = ants.N4BiasFieldCorrection()
_n4.inputs.dimension = 3
_n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
_n4.inputs.bspline_fitting_distance = 95
_n4.inputs.shrink_factor = 2
_n4.inputs.n_iterations = [500,500,500,500]
_n4.inputs.convergence_threshold = 1e-14
_n4.inputs.output_image = '{}/ss__n4_{}_ofM{}.nii.gz'.format(regdir,participant,i)
_n4_res = _n4.run()
#we do this on a separate bias-corrected image to remove hyperintensities which we have to create in order to prevent brain regions being caught by the negative threshold
struct_cutoff = ImageMaths()
struct_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
struct_cutoff.inputs.in_file = _n4_res.outputs.output_image
struct_cutoff_res = struct_cutoff.run()
struct_BET = BET()
struct_BET.inputs.mask = True
struct_BET.inputs.frac = 0.3
struct_BET.inputs.robust = True
struct_BET.inputs.in_file = struct_cutoff_res.outputs.out_file
struct_BET_res = struct_BET.run()
struct_mask = ApplyMask()
struct_mask.inputs.in_file = n4_res.outputs.output_image
struct_mask.inputs.mask_file = struct_BET_res.outputs.mask_file
struct_mask_res = struct_mask.run()
struct_registration = ants.Registration()
struct_registration.inputs.fixed_image = template
struct_registration.inputs.output_transform_prefix = "output_"
struct_registration.inputs.transforms = ['Affine', 'SyN'] ##
struct_registration.inputs.transform_parameters = [(1.0,), (1.0, 3.0, 5.0)] ##
struct_registration.inputs.number_of_iterations = [[2000, 1000, 500], [100, 100, 100]] #
struct_registration.inputs.dimension = 3
struct_registration.inputs.write_composite_transform = True
struct_registration.inputs.collapse_output_transforms = True
struct_registration.inputs.initial_moving_transform_com = True
# Tested on Affine transform: CC takes too long; Demons does not tilt, but moves the slices too far caudally; GC tilts too much on
struct_registration.inputs.metric = ['MeanSquares', 'Mattes']
struct_registration.inputs.metric_weight = [1, 1]
struct_registration.inputs.radius_or_number_of_bins = [16, 32] #
struct_registration.inputs.sampling_strategy = ['Random', None]
struct_registration.inputs.sampling_percentage = [0.3, 0.3]
struct_registration.inputs.convergence_threshold = [1.e-11, 1.e-8] #
struct_registration.inputs.convergence_window_size = [20, 20]
struct_registration.inputs.smoothing_sigmas = [[4, 2, 1], [4, 2, 1]]
struct_registration.inputs.sigma_units = ['vox', 'vox']
struct_registration.inputs.shrink_factors = [[3, 2, 1],[3, 2, 1]]
struct_registration.inputs.use_estimate_learning_rate_once = [True, True]
# if the fixed_image is not acquired similarly to the moving_image (e.g. RARE to histological (e.g. AMBMC)) this should be False
struct_registration.inputs.use_histogram_matching = [False, False]
struct_registration.inputs.winsorize_lower_quantile = 0.005
struct_registration.inputs.winsorize_upper_quantile = 0.98
struct_registration.inputs.args = '--float'
struct_registration.inputs.num_threads = 6
struct_registration.inputs.moving_image = struct_mask_res.outputs.out_file
struct_registration.inputs.output_warped_image = '{}/s_{}_ofM{}.nii.gz'.format(regdir,participant,i)
struct_registration_res = struct_registration.run()
#func
func_n4 = ants.N4BiasFieldCorrection()
func_n4.inputs.dimension = 3
func_n4.inputs.input_image = func_image
func_n4.inputs.bspline_fitting_distance = 100
func_n4.inputs.shrink_factor = 2
func_n4.inputs.n_iterations = [200,200,200,200]
func_n4.inputs.convergence_threshold = 1e-11
func_n4.inputs.output_image = '{}/f_n4_{}_ofM{}.nii.gz'.format(regdir,participant,i)
func_n4_res = func_n4.run()
func_registration = ants.Registration()
func_registration.inputs.fixed_image = n4_res.outputs.output_image
func_registration.inputs.output_transform_prefix = "func_"
func_registration.inputs.transforms = [f2s]
func_registration.inputs.transform_parameters = [(0.1,)]
func_registration.inputs.number_of_iterations = [[40, 20, 10]]
func_registration.inputs.dimension = 3
func_registration.inputs.write_composite_transform = True
func_registration.inputs.collapse_output_transforms = True
func_registration.inputs.initial_moving_transform_com = True
func_registration.inputs.metric = ['MeanSquares']
func_registration.inputs.metric_weight = [1]
func_registration.inputs.radius_or_number_of_bins = [16]
func_registration.inputs.sampling_strategy = ["Regular"]
func_registration.inputs.sampling_percentage = [0.3]
func_registration.inputs.convergence_threshold = [1.e-2]
func_registration.inputs.convergence_window_size = [8]
func_registration.inputs.smoothing_sigmas = [[4, 2, 1]] # [1,0.5,0]
func_registration.inputs.sigma_units = ['vox']
func_registration.inputs.shrink_factors = [[3, 2, 1]]
func_registration.inputs.use_estimate_learning_rate_once = [True]
func_registration.inputs.use_histogram_matching = [False]
func_registration.inputs.winsorize_lower_quantile = 0.005
func_registration.inputs.winsorize_upper_quantile = 0.995
func_registration.inputs.args = '--float'
func_registration.inputs.num_threads = 6
func_registration.inputs.moving_image = func_n4_res.outputs.output_image
func_registration.inputs.output_warped_image = '{}/f_{}_ofM{}.nii.gz'.format(regdir,participant,i)
func_registration_res = func_registration.run()
warp = ants.ApplyTransforms()
warp.inputs.reference_image = template
warp.inputs.input_image_type = 3
warp.inputs.interpolation = 'Linear'
warp.inputs.invert_transform_flags = [False, False]
warp.inputs.terminal_output = 'file'
warp.inputs.output_image = '{}/{}_ofM{}.nii.gz'.format(regdir,participant,i)
warp.num_threads = 6
warp.inputs.input_image = func_image
warp.inputs.transforms = [func_registration_res.outputs.composite_transform, struct_registration_res.outputs.composite_transform]
warp.run()
def func_img_proc(T1C_original, T2_original, FLAIR_original,
T1C_bet, T2_bet, FLAIR_bet, mask_T1C_bet,
T1C_isovoxel, T2_isovoxel, FLAIR_isovoxel, mask_T1C_bet_iso,
T1C_corrected, T2_corrected, FLAIR_corrected, T1C_bet_temp):
##Skull Stripping
t1c_isovoxel = func_resample_isovoxel(T1C_original)
sitk.WriteImage(t1c_isovoxel, T1C_isovoxel)
print("resampling T1C_original - completed")
func_register(T2_original, T1C_isovoxel, T2_isovoxel)
print("register T2_original to T1C_isovoxel - completed")
func_register(FLAIR_original, T1C_isovoxel, FLAIR_isovoxel)
print("register FLAIR_original to T1C_isovoxel - completed")
bet_t1gd_iso = BET(in_file = T1C_isovoxel,
frac = 0.4,
mask = True, # brain tissue mask is stored with '_mask' suffix after T1C_bet.
reduce_bias = True,
out_file = T1C_bet_temp)
bet_t1gd_iso.run()
print("Acquired BET mask...")
os.remove(T1C_bet_temp)
brain_mask_file = T1C_bet_temp[:len(T1C_bet_temp)-len('.nii.gz')] + '_mask.nii.gz'
ApplyBet_T1C = ApplyMask(in_file = T1C_isovoxel,
mask_file= brain_mask_file,
out_file= T1C_bet)
ApplyBet_T1C.run()
ApplyBet_T2 = ApplyMask(in_file = T2_isovoxel,
mask_file= brain_mask_file,
out_file=T2_bet)
ApplyBet_T2.run()
ApplyBet_FLAIR = ApplyMask(in_file = FLAIR_isovoxel,
mask_file= brain_mask_file,
out_file=FLAIR_bet)
ApplyBet_FLAIR.run()
print("Skull stripping of T1C, T2, FLAIR... - done")
### Resampling, REgisgtering BET files
t1c_isovoxel = func_resample_isovoxel(T1C_bet, isseg=False)
sitk.WriteImage(t1c_isovoxel, T1C_isovoxel)
bmask_isovoxel = func_resample_isovoxel(mask_T1C_bet, isseg=True)
sitk.WriteImage(bmask_isovoxel, mask_T1C_bet_iso)
print("resampling T1C & brain mask - completed")
func_register(T2_bet, T1C_isovoxel, T2_isovoxel)
print("register T2 to T1C_isovoxel - completed")
func_register(FLAIR_bet, T1C_isovoxel, FLAIR_isovoxel)
print("register FLAIR to T1C_isovoxel - completed")
### Corrections
func_n4bias(T1C_isovoxel, T1C_corrected)
print("T1C bias correction done...")
func_n4bias(T2_isovoxel, T2_corrected)
print("T2 bias correction done...")
func_n4bias(FLAIR_isovoxel, FLAIR_corrected)
print("FLAIR bias correction done...")
#from mriqc.motion import plot_frame_displacement
#
#test_workflow = Workflow(name="qc_workflow")
#
#test_workflow.base_dir = work_dir
#from nipype import SelectFiles
#templates = dict(T1="*_{subject_id}_*/T1w_MPR_BIC_v1/*00001.nii*")
#file_list = Node(SelectFiles(templates), name = "EPI_and_T1_File_Selection")
#file_list.inputs.base_directory = data_dir
#file_list.iterables = [("subject_id", subject_list), ("p_dir", ["LR", "RL"])]
from nipype.interfaces.fsl import BET
mask_gen = BET()
mask_gen.inputs.in_file = "/Users/craigmoodie/Documents/AA_Connectivity_Psychosis/AA_Connectivity_Analysis/10_Subject_ICA_test/Subject_1/T1w_MPR_BIC_v1/S1543TWL_P126317_1_19_00001.nii"
mask_gen.inputs.mask = True
mask_gen.inputs.out_file = "bet_mean"
mask_gen.run()
#mask_gen = MapNode(BET(), name="Mask_Generation", iterfield="in_file")
#from spatial_qc import summary_mask, ghost_all, ghost_direction, ghost_mask, fwhm, artifacts, efc, cnr, snr
#
#
#summary_mask()
def transform(self, X, y=None):
in_files_search_param = self.gather_steps[1]
if type(X[0]) == str and \
self.backend == 'fsl' and \
self.gather_steps[0] != 'source':
in_files_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.gather_steps[0])
layout = BIDSLayout(in_files_dir)
X.copy()
for subject in X:
in_files = []
for path in layout.get(subject=subject,
**in_files_search_param):
path = path.filename
in_files.append(path)
for in_file in in_files:
path, filename = os.path.split(in_file)
path, directory = os.path.split(path)
inner_structure_path = list()
inner_structure_path.append(directory)
while 'sub-' not in directory:
path, directory = os.path.split(path)
inner_structure_path.append(directory)
inner_structure_path = reversed(inner_structure_path)
out_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.transformer_name,
*inner_structure_path)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_filename = get_brain_filename(filename)
out_file = os.path.join(out_dir, out_filename)
betfsl = BET(in_file=in_file,
out_file=out_file,
**self.backend_param)
betfsl.run()
if type(X[0]) == str and \
self.backend == 'fsl' and \
self.gather_steps[0] == 'source':
in_files_dir = self.project_path
layout = BIDSLayout(in_files_dir)
X = X.copy()
for subject in X:
in_files = []
for path in layout.get(subject=subject,
**in_files_search_param):
path = path.filename
if 'derivatives' not in path.split(os.sep):
in_files.append(path)
for in_file in in_files:
path, filename = os.path.split(in_file)
path, directory = os.path.split(path)
inner_structure_path = list()
inner_structure_path.append(directory)
while 'sub-' not in directory:
path, directory = os.path.split(path)
inner_structure_path.append(directory)
inner_structure_path = reversed(inner_structure_path)
out_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.transformer_name,
*inner_structure_path)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_filename = get_brain_filename(filename)
out_file = os.path.join(out_dir, out_filename)
print(out_file)
betfsl = BET(in_file=in_file,
out_file=out_file,
**self.backend_param)
betfsl.run()
return X
"""Runs bet twice to improve skull-stripping with center of gravity"""
from nipype.interfaces.fsl import BET
anat = "../../data/ds000171/sub-control01/anat/sub-control01_T1w.nii.gz"
bet = BET()
bet.inputs.in_file = anat
bet.inputs.frac = 0.5
brain = '../output/better-bet-anat/anat_bet.nii.gz'
bet.inputs.out_file = brain
bet.inputs.mask = True
bet.run()
# Load mask as numpy array
import nibabel as nib
mask_path = '../output/better-bet-anat/anat_bet_mask.nii.gz'
mask = nib.load(mask_path)
mask_array = mask.get_fdata()
# Get center of mass with scipy
from scipy.ndimage.measurements import center_of_mass
cog = [int(x)
for x in list(center_of_mass(mask_array))] # list comprehension yo!
bet2 = BET()
bet2.inputs.in_file = anat
bet2.inputs.frac = 0.3
bet2.inputs.center = cog
bet2.inputs.out_file = '../output/better-bet-anat/anat_bet2.nii.gz'
def structural_to_functional_per_participant_test(subjects_sessions,
template = "~/GitHub/mriPipeline/templates/waxholm/new/WHS_SD_masked.nii.gz",
f_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_SE_EPI/f_bru2nii/",
s_file_format = "~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_T2_TurboRARE/s_bru2nii/",
num_threads = 3,
):
template = os.path.expanduser(template)
for subject_session in subjects_sessions:
func_image_dir = os.path.expanduser(f_file_format.format(**subject_session))
struct_image_dir = os.path.expanduser(s_file_format.format(**subject_session))
try:
for myfile in os.listdir(func_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
func_image = os.path.join(func_image_dir,myfile)
for myfile in os.listdir(struct_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
struct_image = os.path.join(struct_image_dir,myfile)
except FileNotFoundError:
pass
else:
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200,200,200,200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = '{}_{}_1_biasCorrection_forRegistration.nii.gz'.format(*subject_session.values())
n4_res = n4.run()
_n4 = ants.N4BiasFieldCorrection()
_n4.inputs.dimension = 3
_n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
_n4.inputs.bspline_fitting_distance = 95
_n4.inputs.shrink_factor = 2
_n4.inputs.n_iterations = [500,500,500,500]
_n4.inputs.convergence_threshold = 1e-14
_n4.inputs.output_image = '{}_{}_1_biasCorrection_forMasking.nii.gz'.format(*subject_session.values())
_n4_res = _n4.run()
#we do this on a separate bias-corrected image to remove hyperintensities which we have to create in order to prevent brain regions being caught by the negative threshold
struct_cutoff = ImageMaths()
struct_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
struct_cutoff.inputs.in_file = _n4_res.outputs.output_image
struct_cutoff_res = struct_cutoff.run()
struct_BET = BET()
struct_BET.inputs.mask = True
struct_BET.inputs.frac = 0.3
struct_BET.inputs.robust = True
struct_BET.inputs.in_file = struct_cutoff_res.outputs.out_file
struct_BET.inputs.out_file = '{}_{}_2_brainExtraction.nii.gz'.format(*subject_session.values())
struct_BET_res = struct_BET.run()
# we need/can not apply a fill, because the "holes" if any, will be at the rostral edge (touching it, and thus not counting as holes)
struct_mask = ApplyMask()
struct_mask.inputs.in_file = n4_res.outputs.output_image
struct_mask.inputs.mask_file = struct_BET_res.outputs.mask_file
struct_mask.inputs.out_file = '{}_{}_3_brainMasked.nii.gz'.format(*subject_session.values())
struct_mask_res = struct_mask.run()
struct_registration = ants.Registration()
struct_registration.inputs.fixed_image = template
struct_registration.inputs.output_transform_prefix = "output_"
struct_registration.inputs.transforms = ['Affine', 'SyN'] ##
struct_registration.inputs.transform_parameters = [(1.0,), (1.0, 3.0, 5.0)] ##
struct_registration.inputs.number_of_iterations = [[2000, 1000, 500], [100, 100, 100]] #
struct_registration.inputs.dimension = 3
struct_registration.inputs.write_composite_transform = True
struct_registration.inputs.collapse_output_transforms = True
struct_registration.inputs.initial_moving_transform_com = True
# Tested on Affine transform: CC takes too long; Demons does not tilt, but moves the slices too far caudally; GC tilts too much on
struct_registration.inputs.metric = ['MeanSquares', 'Mattes']
struct_registration.inputs.metric_weight = [1, 1]
struct_registration.inputs.radius_or_number_of_bins = [16, 32] #
struct_registration.inputs.sampling_strategy = ['Random', None]
struct_registration.inputs.sampling_percentage = [0.3, 0.3]
struct_registration.inputs.convergence_threshold = [1.e-11, 1.e-8] #
struct_registration.inputs.convergence_window_size = [20, 20]
struct_registration.inputs.smoothing_sigmas = [[4, 2, 1], [4, 2, 1]]
struct_registration.inputs.sigma_units = ['vox', 'vox']
struct_registration.inputs.shrink_factors = [[3, 2, 1],[3, 2, 1]]
struct_registration.inputs.use_estimate_learning_rate_once = [True, True]
# if the fixed_image is not acquired similarly to the moving_image (e.g. RARE to histological (e.g. AMBMC)) this should be False
struct_registration.inputs.use_histogram_matching = [False, False]
struct_registration.inputs.winsorize_lower_quantile = 0.005
struct_registration.inputs.winsorize_upper_quantile = 0.98
struct_registration.inputs.args = '--float'
struct_registration.inputs.num_threads = num_threads
struct_registration.inputs.moving_image = struct_mask_res.outputs.out_file
struct_registration.inputs.output_warped_image = '{}_{}_4_structuralRegistration.nii.gz'.format(*subject_session.values())
struct_registration_res = struct_registration.run()
warp = ants.ApplyTransforms()
warp.inputs.reference_image = template
warp.inputs.input_image_type = 3
warp.inputs.interpolation = 'Linear'
warp.inputs.invert_transform_flags = [False]
warp.inputs.terminal_output = 'file'
warp.inputs.output_image = '{}_{}_5_functionalWarp.nii.gz'.format(*subject_session.values())
warp.num_threads = num_threads
warp.inputs.input_image = func_image
warp.inputs.transforms = struct_registration_res.outputs.composite_transform
warp.run()
def canonical(
subjects_participants,
regdir,
f2s,
template="~/GitHub/mriPipeline/templates/waxholm/WHS_SD_rat_T2star_v1.01_downsample3.nii.gz",
f_file_format="~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_SE_EPI/f_bru2nii/",
s_file_format="~/GitHub/mripipeline/base/preprocessing/generic_work/_subject_session_{subject}.{session}/_scan_type_T2_TurboRARE/s_bru2nii/",
):
"""Warp a functional image based on the functional-to-structural and the structural-to-template registrations.
Currently this approach is failing because the functiona-to-structural registration pushes the brain stem too far down.
This may be
"""
template = os.path.expanduser(template)
for subject_participant in subjects_participants:
func_image_dir = os.path.expanduser(
f_file_format.format(**subject_participant))
struct_image_dir = os.path.expanduser(
s_file_format.format(**subject_participant))
try:
for myfile in os.listdir(func_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
func_image = os.path.join(func_image_dir, myfile)
for myfile in os.listdir(struct_image_dir):
if myfile.endswith((".nii.gz", ".nii")):
struct_image = os.path.join(struct_image_dir, myfile)
except FileNotFoundError:
pass
else:
#struct
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200, 200, 200, 200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = '{}/ss_n4_{}_ofM{}.nii.gz'.format(
regdir, participant, i)
n4_res = n4.run()
_n4 = ants.N4BiasFieldCorrection()
_n4.inputs.dimension = 3
_n4.inputs.input_image = struct_image
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
_n4.inputs.bspline_fitting_distance = 95
_n4.inputs.shrink_factor = 2
_n4.inputs.n_iterations = [500, 500, 500, 500]
_n4.inputs.convergence_threshold = 1e-14
_n4.inputs.output_image = '{}/ss__n4_{}_ofM{}.nii.gz'.format(
regdir, participant, i)
_n4_res = _n4.run()
#we do this on a separate bias-corrected image to remove hyperintensities which we have to create in order to prevent brain regions being caught by the negative threshold
struct_cutoff = ImageMaths()
struct_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
struct_cutoff.inputs.in_file = _n4_res.outputs.output_image
struct_cutoff_res = struct_cutoff.run()
struct_BET = BET()
struct_BET.inputs.mask = True
struct_BET.inputs.frac = 0.3
struct_BET.inputs.robust = True
struct_BET.inputs.in_file = struct_cutoff_res.outputs.out_file
struct_BET_res = struct_BET.run()
struct_mask = ApplyMask()
struct_mask.inputs.in_file = n4_res.outputs.output_image
struct_mask.inputs.mask_file = struct_BET_res.outputs.mask_file
struct_mask_res = struct_mask.run()
struct_registration = ants.Registration()
struct_registration.inputs.fixed_image = template
struct_registration.inputs.output_transform_prefix = "output_"
struct_registration.inputs.transforms = ['Affine', 'SyN'] ##
struct_registration.inputs.transform_parameters = [(1.0, ),
(1.0, 3.0, 5.0)
] ##
struct_registration.inputs.number_of_iterations = [[
2000, 1000, 500
], [100, 100, 100]] #
struct_registration.inputs.dimension = 3
struct_registration.inputs.write_composite_transform = True
struct_registration.inputs.collapse_output_transforms = True
struct_registration.inputs.initial_moving_transform_com = True
# Tested on Affine transform: CC takes too long; Demons does not tilt, but moves the slices too far caudally; GC tilts too much on
struct_registration.inputs.metric = ['MeanSquares', 'Mattes']
struct_registration.inputs.metric_weight = [1, 1]
struct_registration.inputs.radius_or_number_of_bins = [16, 32] #
struct_registration.inputs.sampling_strategy = ['Random', None]
struct_registration.inputs.sampling_percentage = [0.3, 0.3]
struct_registration.inputs.convergence_threshold = [1.e-11,
1.e-8] #
struct_registration.inputs.convergence_window_size = [20, 20]
struct_registration.inputs.smoothing_sigmas = [[4, 2, 1],
[4, 2, 1]]
struct_registration.inputs.sigma_units = ['vox', 'vox']
struct_registration.inputs.shrink_factors = [[3, 2, 1], [3, 2, 1]]
struct_registration.inputs.use_estimate_learning_rate_once = [
True, True
]
# if the fixed_image is not acquired similarly to the moving_image (e.g. RARE to histological (e.g. AMBMC)) this should be False
struct_registration.inputs.use_histogram_matching = [False, False]
struct_registration.inputs.winsorize_lower_quantile = 0.005
struct_registration.inputs.winsorize_upper_quantile = 0.98
struct_registration.inputs.args = '--float'
struct_registration.inputs.num_threads = 6
struct_registration.inputs.moving_image = struct_mask_res.outputs.out_file
struct_registration.inputs.output_warped_image = '{}/s_{}_ofM{}.nii.gz'.format(
regdir, participant, i)
struct_registration_res = struct_registration.run()
#func
func_n4 = ants.N4BiasFieldCorrection()
func_n4.inputs.dimension = 3
func_n4.inputs.input_image = func_image
func_n4.inputs.bspline_fitting_distance = 100
func_n4.inputs.shrink_factor = 2
func_n4.inputs.n_iterations = [200, 200, 200, 200]
func_n4.inputs.convergence_threshold = 1e-11
func_n4.inputs.output_image = '{}/f_n4_{}_ofM{}.nii.gz'.format(
regdir, participant, i)
func_n4_res = func_n4.run()
func_registration = ants.Registration()
func_registration.inputs.fixed_image = n4_res.outputs.output_image
func_registration.inputs.output_transform_prefix = "func_"
func_registration.inputs.transforms = [f2s]
func_registration.inputs.transform_parameters = [(0.1, )]
func_registration.inputs.number_of_iterations = [[40, 20, 10]]
func_registration.inputs.dimension = 3
func_registration.inputs.write_composite_transform = True
func_registration.inputs.collapse_output_transforms = True
func_registration.inputs.initial_moving_transform_com = True
func_registration.inputs.metric = ['MeanSquares']
func_registration.inputs.metric_weight = [1]
func_registration.inputs.radius_or_number_of_bins = [16]
func_registration.inputs.sampling_strategy = ["Regular"]
func_registration.inputs.sampling_percentage = [0.3]
func_registration.inputs.convergence_threshold = [1.e-2]
func_registration.inputs.convergence_window_size = [8]
func_registration.inputs.smoothing_sigmas = [[4, 2,
1]] # [1,0.5,0]
func_registration.inputs.sigma_units = ['vox']
func_registration.inputs.shrink_factors = [[3, 2, 1]]
func_registration.inputs.use_estimate_learning_rate_once = [True]
func_registration.inputs.use_histogram_matching = [False]
func_registration.inputs.winsorize_lower_quantile = 0.005
func_registration.inputs.winsorize_upper_quantile = 0.995
func_registration.inputs.args = '--float'
func_registration.inputs.num_threads = 6
func_registration.inputs.moving_image = func_n4_res.outputs.output_image
func_registration.inputs.output_warped_image = '{}/f_{}_ofM{}.nii.gz'.format(
regdir, participant, i)
func_registration_res = func_registration.run()
warp = ants.ApplyTransforms()
warp.inputs.reference_image = template
warp.inputs.input_image_type = 3
warp.inputs.interpolation = 'Linear'
warp.inputs.invert_transform_flags = [False, False]
warp.inputs.terminal_output = 'file'
warp.inputs.output_image = '{}/{}_ofM{}.nii.gz'.format(
regdir, participant, i)
warp.num_threads = 6
warp.inputs.input_image = func_image
warp.inputs.transforms = [
func_registration_res.outputs.composite_transform,
struct_registration_res.outputs.composite_transform
]
warp.run()
def structural_per_participant_test(
participant,
conditions=["", "_aF", "_cF1", "_cF2", "_pF"],
template="/home/chymera/ni_data/templates/ds_QBI_chr.nii.gz",
):
for i in conditions:
image_dir = "/home/chymera/ni_data/ofM.dr/preprocessing/generic_work/_subject_session_{}.ofM{}/_scan_type_T2_TurboRARE/s_bru2nii/".format(
participant, i)
print(image_dir)
try:
for myfile in os.listdir(image_dir):
if myfile.endswith(".nii"):
mimage = os.path.join(image_dir, myfile)
except FileNotFoundError:
pass
else:
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = mimage
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200, 200, 200, 200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = 'ss_n4_{}_ofM{}.nii.gz'.format(
participant, i)
n4_res = n4.run()
_n4 = ants.N4BiasFieldCorrection()
_n4.inputs.dimension = 3
_n4.inputs.input_image = mimage
# correction bias is introduced (along the z-axis) if the following value is set to under 85. This is likely contingent on resolution.
_n4.inputs.bspline_fitting_distance = 95
_n4.inputs.shrink_factor = 2
_n4.inputs.n_iterations = [500, 500, 500, 500]
_n4.inputs.convergence_threshold = 1e-14
_n4.inputs.output_image = 'ss__n4_{}_ofM{}.nii.gz'.format(
participant, i)
_n4_res = _n4.run()
#we do this on a separate bias-corrected image to remove hyperintensities which we have to create in order to prevent brain regions being caught by the negative threshold
struct_cutoff = ImageMaths()
struct_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
struct_cutoff.inputs.in_file = _n4_res.outputs.output_image
struct_cutoff_res = struct_cutoff.run()
struct_BET = BET()
struct_BET.inputs.mask = True
struct_BET.inputs.frac = 0.3
struct_BET.inputs.robust = True
struct_BET.inputs.in_file = struct_cutoff_res.outputs.out_file
struct_BET_res = struct_BET.run()
mask = ApplyMask()
mask.inputs.in_file = n4_res.outputs.output_image
mask.inputs.mask_file = struct_BET_res.outputs.mask_file
mask_res = mask.run()
struct_registration = ants.Registration()
struct_registration.inputs.fixed_image = template
struct_registration.inputs.output_transform_prefix = "output_"
struct_registration.inputs.transforms = ['Rigid', 'Affine',
'SyN'] ##
struct_registration.inputs.transform_parameters = [(.1, ), (1.0, ),
(1.0, 3.0, 5.0)
] ##
struct_registration.inputs.number_of_iterations = [[
150, 100, 50
], [2000, 1000, 500], [100, 100, 100]] #
struct_registration.inputs.dimension = 3
struct_registration.inputs.write_composite_transform = True
struct_registration.inputs.collapse_output_transforms = True
struct_registration.inputs.initial_moving_transform_com = True
# Tested on Affine transform: CC takes too long; Demons does not tilt, but moves the slices too far caudally; GC tilts too much on
struct_registration.inputs.metric = [
'MeanSquares', 'MeanSquares', 'Mattes'
]
struct_registration.inputs.metric_weight = [1, 1, 1]
struct_registration.inputs.radius_or_number_of_bins = [16, 16,
32] #
struct_registration.inputs.sampling_strategy = [
'Random', 'Random', None
]
struct_registration.inputs.sampling_percentage = [0.3, 0.3, 0.3]
struct_registration.inputs.convergence_threshold = [
1.e-10, 1.e-11, 1.e-8
] #
struct_registration.inputs.convergence_window_size = [20, 20, 20]
struct_registration.inputs.smoothing_sigmas = [[4, 2,
1], [4, 2, 1],
[4, 2, 1]]
struct_registration.inputs.sigma_units = ['vox', 'vox', 'vox']
struct_registration.inputs.shrink_factors = [[3, 2, 1], [3, 2, 1],
[3, 2, 1]]
struct_registration.inputs.use_estimate_learning_rate_once = [
True, True, True
]
# if the fixed_image is not acquired similarly to the moving_image (e.g. RARE to histological (e.g. AMBMC)) this should be False
struct_registration.inputs.use_histogram_matching = [
False, False, False
]
struct_registration.inputs.winsorize_lower_quantile = 0.005
struct_registration.inputs.winsorize_upper_quantile = 0.98
struct_registration.inputs.args = '--float'
struct_registration.inputs.num_threads = 6
struct_registration.inputs.moving_image = mask_res.outputs.out_file
struct_registration.inputs.output_warped_image = 'ss_{}_ofM{}.nii.gz'.format(
participant, i)
res = struct_registration.run()
"""Test FSL's Brain Extraction Interface""" from nipype.interfaces.fsl import BET bet = BET() bet.inputs.in_file = 'test-data/haxby2001/subj2/anat.nii.gz' bet.inputs.out_file = 'output-fsl-bet/anat_bet.nii.gz' bet.cmdline bet.run()
def get_nuisance_mask(input,
pathSPM,
deformation,
path_output,
nerode_white=1,
nerode_csf=1,
segmentation=True,
cleanup=True):
"""
This function calculates WM and CSF masks in space of the functional time series. It uses SPM
to compute WM and CSF probability maps. These maps are masked with a skullstrip mask and
transformed to native epi space.
Inputs:
*input: input anatomy (orig.mgz).
*pathSPM: path to spm toolbox.
*deformation: coordinate mapping for ana to epi transformation.
*path_output: path where output is saved.
*nerode_white: number of wm mask eroding steps.
*nerode_csf: number of csf mask eroding steps.
*segmentation: do not calculate new masks to not rerun everything.
*cleanup: delete intermediate files.
created by Daniel Haenelt
Date created: 01-03-2019
Last modified: 01-03-2019
"""
import os
import shutil as sh
import nibabel as nb
from scipy.ndimage.morphology import binary_erosion
from nipype.interfaces.fsl import BET
from nipype.interfaces.freesurfer.preprocess import MRIConvert
from nighres.registration import apply_coordinate_mappings
from lib.skullstrip.skullstrip_spm12 import skullstrip_spm12
# make output folder
if not os.path.exists(path_output):
os.mkdir(path_output)
# get filename without file extension of input file
file = os.path.splitext(os.path.basename(input))[0]
# convert to nifti format
mc = MRIConvert()
mc.inputs.in_file = input
mc.inputs.out_file = os.path.join(path_output, file + ".nii")
mc.inputs.out_type = "nii"
mc.run()
# bet skullstrip mask
btr = BET()
btr.inputs.in_file = os.path.join(path_output, file + ".nii")
btr.inputs.frac = 0.5
btr.inputs.mask = True
btr.inputs.no_output = True
btr.inputs.out_file = os.path.join(path_output, "bet")
btr.inputs.output_type = "NIFTI"
btr.run()
# segmentation
if segmentation:
skullstrip_spm12(os.path.join(path_output, file + ".nii"), pathSPM,
path_output)
# load tissue maps
wm_array = nb.load(os.path.join(path_output, "skull",
"c2" + file + ".nii")).get_fdata()
csf_array = nb.load(
os.path.join(path_output, "skull", "c3" + file + ".nii")).get_fdata()
mask_array = nb.load(os.path.join(path_output, "bet_mask.nii")).get_fdata()
# binarize
wm_array[wm_array > 0] = 1
csf_array[csf_array > 0] = 1
# apply brain mask
wm_array = wm_array * mask_array
csf_array = csf_array * mask_array
# erode wm
wm_array = binary_erosion(
wm_array,
structure=None,
iterations=nerode_white,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False,
)
# erode csf
csf_array = binary_erosion(
csf_array,
structure=None,
iterations=nerode_csf,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False,
)
# write wm and csf mask
data_img = nb.load(input)
wm_out = nb.Nifti1Image(wm_array, data_img.affine, data_img.header)
nb.save(wm_out, os.path.join(path_output, "wm_mask_orig.nii"))
csf_out = nb.Nifti1Image(csf_array, data_img.affine, data_img.header)
nb.save(csf_out, os.path.join(path_output, "csf_mask_orig.nii"))
# apply deformation to mask
apply_coordinate_mappings(
os.path.join(path_output, "wm_mask_orig.nii"), # input
deformation, # cmap
interpolation="nearest", # nearest or linear
padding="zero", # closest, zero or max
save_data=True, # save output data to file (boolean)
overwrite=True, # overwrite existing results (boolean)
output_dir=path_output, # output directory
file_name="wm_mask" # base name with file extension for output
)
apply_coordinate_mappings(
os.path.join(path_output, "csf_mask_orig.nii"), # input
deformation, # cmap
interpolation="nearest", # nearest or linear
padding="zero", # closest, zero or max
save_data=True, # save output data to file (boolean)
overwrite=True, # overwrite existing results (boolean)
output_dir=path_output, # output directory
file_name="csf_mask" # base name with file extension for output
)
# rename transformed masks
os.rename(os.path.join(path_output, "wm_mask_def-img.nii.gz"),
os.path.join(path_output, "wm_mask.nii.gz"))
os.rename(os.path.join(path_output, "csf_mask_def-img.nii.gz"),
os.path.join(path_output, "csf_mask.nii.gz"))
# cleanup
if cleanup:
os.remove(os.path.join(path_output, "bet_mask.nii"))
os.remove(os.path.join(path_output, "csf_mask_orig.nii"))
os.remove(os.path.join(path_output, "wm_mask_orig.nii"))
os.remove(os.path.join(path_output, "orig.nii"))
sh.rmtree(os.path.join(path_output, "skull"), ignore_errors=True)
def functional_per_participant_test():
for i in ["","_aF","_cF1","_cF2","_pF"]:
template = "~/ni_data/templates/ds_QBI_chr.nii.gz"
participant = "4008"
image_dir = "~/ni_data/ofM.dr/preprocessing/generic_work/_subject_session_{}.ofM{}/_scan_type_7_EPI_CBV/temporal_mean/".format(participant,i)
try:
for myfile in os.listdir(image_dir):
if myfile.endswith(".nii.gz"):
mimage = os.path.join(image_dir,myfile)
except FileNotFoundError:
pass
else:
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = mimage
n4.inputs.bspline_fitting_distance = 100
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [200,200,200,200]
n4.inputs.convergence_threshold = 1e-11
n4.inputs.output_image = 'n4_{}_ofM{}.nii.gz'.format(participant,i)
n4_res = n4.run()
functional_cutoff = ImageMaths()
functional_cutoff.inputs.op_string = "-thrP 30"
functional_cutoff.inputs.in_file = n4_res.outputs.output_image
functional_cutoff_res = functional_cutoff.run()
functional_BET = BET()
functional_BET.inputs.mask = True
functional_BET.inputs.frac = 0.5
functional_BET.inputs.in_file = functional_cutoff_res.outputs.out_file
functional_BET_res = functional_BET.run()
registration = ants.Registration()
registration.inputs.fixed_image = template
registration.inputs.output_transform_prefix = "output_"
registration.inputs.transforms = ['Affine', 'SyN']
registration.inputs.transform_parameters = [(0.1,), (3.0, 3.0, 5.0)]
registration.inputs.number_of_iterations = [[10000, 10000, 10000], [100, 100, 100]]
registration.inputs.dimension = 3
registration.inputs.write_composite_transform = True
registration.inputs.collapse_output_transforms = True
registration.inputs.initial_moving_transform_com = True
registration.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
registration.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
registration.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
registration.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
registration.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
registration.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
registration.inputs.convergence_window_size = [20] * 2 + [5]
registration.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
registration.inputs.sigma_units = ['vox'] * 3
registration.inputs.shrink_factors = [[3, 2, 1]]*2 + [[4, 2, 1]]
registration.inputs.use_estimate_learning_rate_once = [True] * 3
registration.inputs.use_histogram_matching = [False] * 2 + [True]
registration.inputs.winsorize_lower_quantile = 0.005
registration.inputs.winsorize_upper_quantile = 0.995
registration.inputs.args = '--float'
registration.inputs.num_threads = 4
registration.plugin_args = {'qsub_args': '-pe orte 4', 'sbatch_args': '--mem=6G -c 4'}
registration.inputs.moving_image = functional_BET_res.outputs.out_file
registration.inputs.output_warped_image = '{}_ofM{}.nii.gz'.format(participant,i)
res = registration.run()
