def create_peri_mask(timepoint, group, subject, na=[15]):
in_dir = os.path.join(pt.proc_in_dir, timepoint, group, subject, 'T2w')
out_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'T2w')
if not os.path.isdir(in_dir):
sys.exit("Error: '%s' is not an existing directory." % (in_dir, ))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# input atlas labels file (NIfTI)
#path_atlas = os.path.join(in_dir, subject + 'BiasBet_AnnorsfMRI.nii.gz')
# input mask file (NIfTI)
path_in_mask = os.path.join(in_dir, subject + 'Stroke_mask.nii.gz')
if not os.path.isfile(path_in_mask):
sys.exit("Error: '%s' is not a regular file." % (path_in_mask, ))
# output cortex ROIs file (NIfTI)
#path_out_rois = os.path.join(out_dir, subject + '_cortex_rois_1.nii.gz')
mask, mask_dims = pt.read_data(path_in_mask)
#rois, rois_dims = create_rois_1(pt.path_labels_1, path_atlas, path_rois=path_out_rois)
for model in range(3, 4):
for n in na:
# output mask file (NIfTI)
path_out_mask = os.path.join(
out_dir, subject + '_peri_mask_m%d_n%d.nii.gz' % (model, n))
# output masked cortex ROIs file (NIfTI)
#path_out_rois = os.path.join(out_dir, subject + '_cortex_rois_1_m%d_n%d.nii.gz' % (model, n))
peri = np.copy(mask)
if (model == 1) or (model == 2):
for k in zrange(mask.shape[2]):
image = mask[:, :, k]
if np.any(image.astype(np.bool)):
peri[:, :, k] = dm.dilate_repeat(image,
connectivity=model,
n=n)
else:
struct = dm.circle_mask(n=n)
for k in zrange(mask.shape[2]):
image = mask[:, :, k]
if np.any(image.astype(np.bool)):
peri[:, :, k] = dm.dilate_struct(image, struct)
pt.save_data(peri.astype(np.float32),
mask_dims,
path_out_mask,
dtype=None)
def create_rois_1(path_labels, path_atlas, path_rois=None, mask=None):
if not os.path.isfile(path_atlas):
sys.exit("Error: '%s' is not a regular file." % (path_atlas, ))
# create atlas labels ROIs
labels_hdr, rois = csr.create_rois(path_labels, [path_atlas],
datatype=16,
preserve=True)
voxel_dims = labels_hdr[0].get_zooms()
rois = np.squeeze(rois)
# apply mask to ROIs
if mask is not None:
rois = np.multiply(rois, mask)
# save ROIs file (NIfTI)
if path_rois is not None:
pt.save_data(rois, voxel_dims, path_rois, dtype=None)
return (rois, voxel_dims)
def create_rois_2(path_labels,
path_atlas,
path_rois=None,
mask=None,
preserve=False):
if not os.path.isfile(path_atlas):
sys.exit("Error: '%s' is not a regular file." % (path_atlas, ))
# create atlas labels ROIs hyperstack (4D)
labels_hdr, rois = csr.create_rois(path_labels, [path_atlas],
datatype=16,
preserve=preserve)
voxel_dims = labels_hdr[0].get_zooms()
# apply mask to each ROI
if mask is not None:
for k in zrange(rois.shape[3]):
rois[:, :, :, k] = np.multiply(rois[:, :, :, k], mask)
# save ROIs file (NIfTI)
if path_rois is not None:
pt.save_data(rois, voxel_dims, path_rois, dtype=None)
return (rois, voxel_dims)
return dilated
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Dilate input mask.')
parser.add_argument('in_mask', help='input mask file name')
parser.add_argument('-o',
'--out_mask',
help='output mask file name',
required=True)
args = parser.parse_args()
# input mask file
if not os.path.isfile(args.in_mask):
sys.exit("Error: '%s' is not a regular file." % (args.in_mask, ))
# read input mask data
data, voxel_dims = pt.read_data(args.in_mask)
struct = circle_mask()
for k in zrange(data.shape[2]):
image = data[:, :, k]
if np.any(image.astype(np.bool)):
#data[:, :, k] = dilate_repeat(image)
data[:, :, k] = dilate_struct(image, struct)
# save mask data as NIfTI file
pt.save_data(data, voxel_dims, args.out_mask, dtype=None)
def xfm_T2w_rsfMRI(raw_dir, timepoint_P7, timepoint, group, subject, expno_T2w,
expno_rsfMRI, procno_T2w, procno_rsfMRI):
if (expno_T2w is None) or (expno_rsfMRI is None) or (
procno_T2w is None) or (procno_rsfMRI is None):
return
in_dir = os.path.join(pt.proc_in_dir, timepoint, group, subject, 'T2w')
mask_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'T2w')
out_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'fMRI')
if not os.path.isdir(in_dir):
sys.exit("Error: '%s' is not an existing directory." % (in_dir, ))
if not os.path.isdir(mask_dir):
sys.exit("Error: '%s' is not an existing directory." % (mask_dir, ))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# input T2w atlas labels file
path_in_anno = os.path.join(in_dir, subject + 'BiasBet_AnnorsfMRI.nii.gz')
if not os.path.isfile(path_in_anno):
sys.exit("Error: '%s' is not a regular file." % (path_in_anno, ))
# input T2w stroke mask file
if timepoint == timepoint_P7:
path_in_mask = os.path.join(in_dir, subject + 'Stroke_mask.nii.gz')
else:
path_in_mask = os.path.join(mask_dir, subject + 'Stroke_mask.nii.gz')
# input T2w peri-infarct mask file
path_in_peri = os.path.join(mask_dir, subject + '_peri_mask_m3_n15.nii.gz')
if not os.path.isfile(path_in_peri):
sys.exit("Error: '%s' is not a regular file." % (path_in_peri, ))
# output rsfMRI file
#path_rsfMRI = os.path.join(out_dir, subject + '_rsfMRI.nii.gz')
# output transformed T2w file
path_T2w_rsfMRI = os.path.join(out_dir, subject + '_T2w_rsfMRI.nii.gz')
# output transformed atlas labels file
path_anno_rsfMRI = os.path.join(out_dir,
subject + '_T2w_Anno_rsfMRI.nii.gz')
# output transformed stroke mask file
path_mask_rsfMRI = os.path.join(out_dir,
subject + '_T2w_Stroke_mask_rsfMRI.nii.gz')
# output transformed peri-infarct mask file
path_peri_rsfMRI = os.path.join(out_dir,
subject + '_T2w_peri_mask_rsfMRI.nii.gz')
pvr.check_args(pt.proc_out_dir, raw_dir, subject, expno_T2w, procno_T2w)
pvr.check_args(pt.proc_out_dir, raw_dir, subject, expno_rsfMRI,
procno_rsfMRI)
# T2w data
pv = pvr.ParaVision(os.path.join(pt.proc_out_dir, timepoint, group),
raw_dir, subject, expno_T2w, procno_T2w)
pv.read_2dseq(map_raw=False,
map_pv6=False,
roll_fg=False,
squeeze=False,
compact=False,
swap_vd=False,
scale=1.0)
#pv.save_nifti(ftype='NIFTI_GZ')
matrix_T2w, matrix_T2w_inv = pv.get_matrix()
data_T2w = pv.nifti_image.get_data()
#data_dims_T2w = pv.data_dims[:3]
#data_type_T2w = pv.data_type
voxel_dims_T2w = pv.voxel_dims[:3]
#voxel_unit_T2w = pv.voxel_unit
# rsfMRI data
pv = pvr.ParaVision(os.path.join(pt.proc_out_dir, timepoint, group),
raw_dir, subject, expno_rsfMRI, procno_rsfMRI)
pv.read_2dseq(map_raw=False,
map_pv6=False,
roll_fg=False,
squeeze=False,
compact=False,
swap_vd=False,
scale=1.0)
#pv.save_nifti(ftype='NIFTI_GZ')
matrix_rsfMRI, matrix_rsfMRI_inv = pv.get_matrix()
#data_rsfMRI = np.mean(pv.nifti_image.get_data(), axis=3)
data_dims_rsfMRI = pv.data_dims[:3]
#data_type_rsfMRI = pv.data_type
voxel_dims_rsfMRI = pv.voxel_dims[:3]
#voxel_unit_rsfMRI = pv.voxel_unit
# transformation matrix
matrix_T2w_rsfMRI = np.dot(matrix_rsfMRI_inv, matrix_T2w)
matrix_rsfMRI_T2w = np.dot(matrix_T2w_inv, matrix_rsfMRI)
pt.save_matrix(os.path.join(out_dir, subject + '_T2w_rsfMRI.mat'),
matrix_T2w_rsfMRI)
pt.save_matrix(os.path.join(out_dir, subject + '_rsfMRI_T2w.mat'),
matrix_rsfMRI_T2w)
# save rsfMRI data as NIfTI file
#pt.save_data(np.rot90(data_rsfMRI, k=2, axes=(0, 2)), voxel_dims_rsfMRI, path_rsfMRI, dtype=None)
# save transformed T2w data as NIfTI file
data_T2w_rsfMRI = ax.xfm_serial(data_T2w,
matrix_rsfMRI_T2w,
data_dims_rsfMRI,
voxel_dims_rsfMRI,
voxel_dims_T2w,
interp=1,
inverse=True)
pt.save_data(np.rot90(data_T2w_rsfMRI, k=2, axes=(0, 2)),
voxel_dims_rsfMRI,
path_T2w_rsfMRI,
dtype=None)
# save transformed T2w atlas labels as NIfTI file
data_anno, voxel_dims_anno = pt.read_data(path_in_anno)
data_anno_rsfMRI = ax.xfm_serial(np.rot90(data_anno, k=2, axes=(0, 2)),
matrix_rsfMRI_T2w,
data_dims_rsfMRI,
voxel_dims_rsfMRI,
voxel_dims_anno,
interp=0,
inverse=True)
pt.save_data(np.rot90(data_anno_rsfMRI, k=2, axes=(0, 2)),
voxel_dims_rsfMRI,
path_anno_rsfMRI,
dtype=None)
# save transformed T2w stroke mask as NIfTI file
if os.path.isfile(path_in_mask):
data_mask, voxel_dims_mask = pt.read_data(path_in_mask)
data_mask_rsfMRI = ax.xfm_serial(np.rot90(data_mask, k=2, axes=(0, 2)),
matrix_rsfMRI_T2w,
data_dims_rsfMRI,
voxel_dims_rsfMRI,
voxel_dims_mask,
interp=0,
inverse=True)
pt.save_data(np.rot90(data_mask_rsfMRI, k=2, axes=(0, 2)),
voxel_dims_rsfMRI,
path_mask_rsfMRI,
dtype=None)
# save transformed T2w peri-infarct mask as NIfTI file
data_peri, voxel_dims_peri = pt.read_data(path_in_peri)
data_peri_rsfMRI = ax.xfm_serial(np.rot90(data_peri, k=2, axes=(0, 2)),
matrix_rsfMRI_T2w,
data_dims_rsfMRI,
voxel_dims_rsfMRI,
voxel_dims_peri,
interp=0,
inverse=True)
pt.save_data(np.rot90(data_peri_rsfMRI, k=2, axes=(0, 2)),
voxel_dims_rsfMRI,
path_peri_rsfMRI,
dtype=None)
def create_rois_DTI(timepoint, group, subject, labels, labels_2, scale=10.0):
in_dir = os.path.join(pt.proc_in_dir, timepoint, group, subject, 'DTI')
out_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'DTI')
dti_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'DTI',
'DSI_studio')
if not os.path.isdir(in_dir):
#sys.exit("Error: '%s' is not an existing directory." % (in_dir,))
return
if not os.path.isdir(out_dir):
sys.exit("Error: '%s' is not an existing directory." % (out_dir, ))
if not os.path.exists(dti_dir):
os.makedirs(dti_dir)
# input atlas labels file (NIfTI)
#path_atlas = os.path.join(in_dir, subject + 'SmoothMicoBet_AnnoSplit_rsfMRI.nii.gz')
path_atlas = os.path.join(out_dir, subject + '_T2w_Anno_DTI.nii.gz')
if not os.path.isfile(path_atlas):
#sys.exit("Error: '%s' is not a regular file." % (path_atlas,))
return
# input peri-infarct mask file (NIfTI)
path_mask = os.path.join(out_dir, subject + '_T2w_peri_mask_DTI.nii.gz')
peri_mask, _ = read_mask(path_mask)
# output ROIs files (NIfTI)
#path_out_rois = os.path.join(out_dir, subject + '_seed_rois.nii.gz')
#path_out_rois_1 = os.path.join(out_dir, subject + '_cortex_rois_1.nii.gz')
#path_out_rois_2 = os.path.join(out_dir, subject + '_cortex_rois_2.nii.gz')
#path_out_rois_x = os.path.join(out_dir, subject + '_seed_rois_mod.nii.gz')
path_out_rois = os.path.join(out_dir, 'Seed_ROIs_all.nii.gz')
path_out_rois_1 = os.path.join(out_dir, subject + '_rois_peri.nii.gz')
path_out_rois_2 = os.path.join(out_dir, 'Seed_ROIs_peri.nii.gz')
path_out_rois_x = os.path.join(out_dir, 'Seed_ROIs_all_mod_peri.nii.gz')
# output maximum intensity projection files (NIfTI)
path_out_mip_rois = os.path.join(dti_dir,
subject + '_T2w_Anno_DTI_scaled.nii.gz')
path_out_mip_rois_x = os.path.join(
dti_dir, subject + '_T2w_Anno_DTI_mod_peri_scaled.nii.gz')
# output maximum intensity projection label names text files
path_out_label_names = os.path.join(
dti_dir, subject + '_T2w_Anno_DTI_scaled.nii.txt')
path_out_label_names_x = os.path.join(
dti_dir, subject + '_T2w_Anno_DTI_mod_peri_scaled.nii.txt')
rois, rois_dims = create_rois_2(pt.path_labels,
path_atlas,
path_rois=path_out_rois,
preserve=True)
_, _ = create_rois_1(pt.path_labels_1,
path_atlas,
path_rois=path_out_rois_1,
mask=peri_mask)
rois_2, _ = create_rois_2(pt.path_labels_2,
path_atlas,
path_rois=path_out_rois_2,
mask=peri_mask,
preserve=True)
rois_x = np.copy(rois)
replace_rois(labels, labels_2, rois_x, rois_2)
pt.save_data(rois_x, rois_dims, path_out_rois_x, dtype=None)
mip_rois = np.max(rois, axis=3)
mip_rois_x = np.max(rois_x, axis=3)
mip_rois_dims = tuple((x * scale) for x in rois_dims)
pt.save_data(mip_rois, mip_rois_dims, path_out_mip_rois, dtype=None)
pt.save_data(mip_rois_x, mip_rois_dims, path_out_mip_rois_x, dtype=None)
shutil.copyfile(pt.path_label_names_2000, path_out_label_names)
shutil.copyfile(pt.path_label_names_2000, path_out_label_names_x)
print(path_out_label_names)
print(path_out_label_names_x)
def create_rois_rsfMRI(timepoint, group, subject, labels, labels_2,
label_names_2000, label_names_peri):
in_dir = os.path.join(pt.proc_in_dir, timepoint, group, subject, 'fMRI')
out_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'fMRI')
regr_dir = os.path.join(pt.proc_out_dir, timepoint, group, subject, 'fMRI',
'regr')
if not os.path.isdir(in_dir):
sys.exit("Error: '%s' is not an existing directory." % (in_dir, ))
if not os.path.isdir(out_dir):
sys.exit("Error: '%s' is not an existing directory." % (out_dir, ))
if not os.path.exists(regr_dir):
os.makedirs(regr_dir)
# input atlas labels file (NIfTI)
#path_atlas = os.path.join(in_dir, subject + 'SmoothBet_AnnoSplit_rsfMRI.nii.gz')
path_atlas = os.path.join(out_dir, subject + '_T2w_Anno_rsfMRI.nii.gz')
# input peri-infarct mask file (NIfTI)
path_mask = os.path.join(out_dir, subject + '_T2w_peri_mask_rsfMRI.nii.gz')
peri_mask, _ = read_mask(path_mask)
# input rsfMRI file (NIfTI)
path_rsfMRI = os.path.join(in_dir, 'regr', subject + '_mcf_f_SFRGR.nii.gz')
if not os.path.isfile(path_rsfMRI):
sys.exit("Error: '%s' is not a regular file." % (path_rsfMRI, ))
# output ROIs files (NIfTI)
#path_out_rois = os.path.join(out_dir, subject + '_seed_rois.nii.gz')
#path_out_rois_1 = os.path.join(out_dir, subject + '_cortex_rois_1.nii.gz')
#path_out_rois_2 = os.path.join(out_dir, subject + '_cortex_rois_2.nii.gz')
#path_out_rois_x = os.path.join(out_dir, subject + '_seed_rois_mod.nii.gz')
path_out_rois = os.path.join(out_dir, 'Seed_ROIs_all.nii.gz')
path_out_rois_1 = os.path.join(out_dir, subject + '_rois_peri.nii.gz')
path_out_rois_2 = os.path.join(out_dir, 'Seed_ROIs_peri.nii.gz')
path_out_rois_x = os.path.join(out_dir, 'Seed_ROIs_all_mod_peri.nii.gz')
# output time series text files
#path_out_ts = os.path.join(out_dir, subject + '_ts.txt')
#path_out_ts_2 = os.path.join(out_dir, subject + '_ts_cortex.txt')
#path_out_ts_x = os.path.join(out_dir, subject + '_ts_mod.txt')
path_out_ts = os.path.join(regr_dir, 'MasksTCsSplit_GV_all.txt')
path_out_ts_2 = os.path.join(regr_dir, 'MasksTCsSplit_GV_peri.txt')
path_out_ts_x = os.path.join(regr_dir, 'MasksTCsSplit_GV_all_mod_peri.txt')
rois, rois_dims = create_rois_2(pt.path_labels,
path_atlas,
path_rois=path_out_rois)
_, _ = create_rois_1(pt.path_labels_1,
path_atlas,
path_rois=path_out_rois_1,
mask=peri_mask)
rois_2, _ = create_rois_2(pt.path_labels_2,
path_atlas,
path_rois=path_out_rois_2,
mask=peri_mask)
rois_x = np.copy(rois)
replace_rois(labels, labels_2, rois_x, rois_2)
pt.save_data(rois_x, rois_dims, path_out_rois_x, dtype=None)
mts.mean_ts(path_rsfMRI, path_out_rois, path_out_ts, label_names_2000)
mts.mean_ts(path_rsfMRI, path_out_rois_2, path_out_ts_2, label_names_peri)
mts.mean_ts(path_rsfMRI, path_out_rois_x, path_out_ts_x, label_names_2000)
# input labels text file with atlas index and seed regions (labels) in each line
# Atlas (1 or 2), Label 1, Label 2, ...
if len(args.in_labels) > 0: path_labels = args.in_labels
if not os.path.isfile(path_labels):
sys.exit("Error: '%s' is not a regular file." % (path_labels, ))
# input atlas labels files (NIfTI)
if len(args.in_atlas) > 0: list_atlas = args.in_atlas
for path_atlas in list_atlas:
if not os.path.isfile(path_atlas):
sys.exit("Error: '%s' is not a regular file." % (path_atlas, ))
# output seed ROIs file
path_rois = os.path.join(
os.getcwd(), args.out_rois) if args.out_rois != None else os.path.join(
os.path.dirname(list_atlas[0]), file_name)
# get date and time
#print(pt.get_date())
# create atlas labels hyperstack (4D)
labels_hdr, rois = create_rois(path_labels,
path_atlas,
datatype=args.datatype,
preserve=args.preserve)
# save atlas labels file
voxel_dims = labels_hdr[0].get_zooms()
pt.save_data(rois, voxel_dims, path_rois, dtype=None)
if not os.path.isfile(args.in_data):
sys.exit("Error: '%s' is not a regular file." % (args.in_data,))
# input matrix text file
if not os.path.isfile(args.in_matrix):
sys.exit("Error: '%s' is not a regular file." % (args.in_matrix,))
# output shape
out_shape = '' if args.out_shape is None else args.out_shape
# output voxel dimensions
out_dims = '' if args.out_dims is None else args.out_dims
# interpolation method (0: nearestneighbour, 1: trilinear)
interp = 1 if args.interp is None else args.interp
# read input data
data, voxel_dims = pt.read_data(args.in_data)
# read matrix text file
matrix = make_matrix(pt.read_text(args.in_matrix))
data_dims_xfm = tuple(int(v) for v in out_shape.split(',')) if len(out_shape) > 0 else data.shape[:]
voxel_dims_xfm = tuple(float(v) for v in out_dims.split(',')) if len(out_dims) > 0 else voxel_dims[:]
# transform input data
data_xfm = xfm_serial(data, matrix, data_dims_xfm, voxel_dims_xfm, voxel_dims, interp=interp, inverse=args.inverse)
# save transformed data as NIfTI file
pt.save_data(data_xfm, voxel_dims_xfm, args.out_data, dtype=None)