def resample_data_resolution(dir_src, dir_out, verbose=False):
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_out, 'data_' + par_b_tag + '.nii.gz')
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
data2, affine2 = reslice(data, affine, (1.25,) * 3, (par_dim_vox,) * 3)
mask2, _ = reslice(mask, affine, (1.25,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
fname = 'nodif_brain_mask_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), mask2, affine2)
fwmparc = pjoin(dir_src, '../wmparc.nii.gz')
data, affine = load_nifti(fwmparc, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'wmparc_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
ft1w = pjoin(dir_src, '../T1w_acpc_dc_restore_brain.nii.gz')
data, affine = load_nifti(ft1w, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0, mode='constant')
fname = 't1w_acpc_dc_restore_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
def resample_data_resolution(dir_src, dir_out, verbose=False):
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_out, 'data_' + par_b_tag + '.nii.gz')
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
data2, affine2 = reslice(data, affine, (1.25,) * 3, (par_dim_vox,) * 3)
mask2, _ = reslice(mask, affine, (1.25,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
fname = 'nodif_brain_mask_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), mask2, affine2)
fwmparc = pjoin(dir_src, '../wmparc.nii.gz')
data, affine = load_nifti(fwmparc, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'wmparc_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
ft1w = pjoin(dir_src, '../T1w_acpc_dc_restore_brain.nii.gz')
data, affine = load_nifti(ft1w, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0, mode='constant')
fname = 't1w_acpc_dc_restore_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
def resample(img, pixdim=1.5, ref_file=None):
d = img.get_data().astype(np.float64)
# option to align to reference volume
if ref_file!=None:
# NOT WORKING! I don't think the dipy registration routine is applying the affine.
ref = nb.load(ref_file)
mn = nb.Nifti1Image(d.mean(axis=3), img.get_affine())
reg = registration.HistogramRegistration(mn, ref, interp='tri')
T = reg.optimize('rigid')
resamp_xform = np.dot(img.get_affine(), T.inv().as_affine())
else:
resamp_xform = img.get_affine()
try:
from dipy.align.aniso2iso import reslice
except:
from dipy.align.aniso2iso import resample as reslice
data,xform = reslice(d, resamp_xform, img.get_header().get_zooms()[:3], [pixdim]*3, order=5)
return nb.Nifti1Image(data, xform)
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
factor_split = param.factor.split('x')
factor = [float(factor_split[i]) for i in range(len(factor_split))]
# check if it has three values
if not len(factor) == 3:
sct.printv('\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
else:
fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))]
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = path_data, file_data, ext_data
if param.fname_out != '':
file_out = sct.extract_fname(param.fname_out)[1]
else:
file_out.append(param.file_suffix)
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
#TODO : adapt for 2D too or change description
sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
nx_new = int(round(nx*fx))
ny_new = int(round(ny*fy))
nz_new = int(round(nz*fz))
px_new = px/fx
py_new = py/fy
pz_new = pz/fz
sct.printv(' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new)+ ' x ' + str(nt), param.verbose)
zooms = input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv('WARNING: wrong input for the interpolation. Using default value = trilinear', param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)
new_im = Image(param=new_data)
new_im.absolutepath = path_out+file_out+ext_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')
print
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
factor_split = param.factor.split('x')
factor = [float(factor_split[i]) for i in range(len(factor_split))]
# check if it has three values
if not len(factor) == 3:
sct.printv(
'\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1,
'error')
else:
fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))]
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = path_data, file_data, ext_data
if param.fname_out != '':
file_out = sct.extract_fname(param.fname_out)[1]
else:
file_out.append(param.file_suffix)
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
#TODO : adapt for 2D too or change description
sct.run(
'ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program',
param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
nx_new = int(round(nx * fx))
ny_new = int(round(ny * fy))
nz_new = int(round(nz * fz))
px_new = px / fx
py_new = py / fy
pz_new = pz / fz
sct.printv(
' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new) +
' x ' + str(nt), param.verbose)
zooms = input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation
) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv(
'WARNING: wrong input for the interpolation. Using default value = trilinear',
param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data,
affine,
zooms,
new_zooms,
mode=param.mode,
order=order)
new_im = Image(param=new_data)
new_im.absolutepath = path_out + file_out + ext_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview ' + param.fname_out + ' &', param.verbose, 'info')
print
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
new_size_split = param.new_size.split('x')
new_size = [float(new_size_split[i]) for i in range(len(new_size_split))]
# check if it has three values
if not len(new_size) == 3:
sct.printv('\nERROR: new size should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
else:
ns_x, ns_y, ns_z = new_size
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = '', file_data, ext_data
if param.fname_out != '':
path_out, file_out, ext_out = sct.extract_fname(param.fname_out)
else:
file_out += param.file_suffix
param.fname_out = path_out+file_out+ext_out
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(' ' + str(px) + ' x ' + str(py) + ' x ' + str(pz)+ ' x ' + str(pt)+'mm', param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
if param.new_size_type == 'factor':
px_new = px/ns_x
py_new = py/ns_y
pz_new = pz/ns_z
elif param.new_size_type == 'vox':
px_new = px*nx/ns_x
py_new = py*ny/ns_y
pz_new = pz*nz/ns_z
else:
px_new = ns_x
py_new = ns_y
pz_new = ns_z
sct.printv(' ' + str(px_new) + ' x ' + str(py_new) + ' x ' + str(pz_new)+ ' x ' + str(pt)+'mm', param.verbose)
zooms = (px, py, pz) # input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_qform() # get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv('WARNING: wrong input for the interpolation. Using default value = linear', param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)
new_im = Image(param=new_data)
new_im.absolutepath = param.fname_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
# Set the new sform and qform:
new_im.hdr.set_sform(new_affine)
new_im.hdr.set_qform(new_affine)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')
