# read the sinogram
sino_hq = ntp.read_image(fname)
na = sino_hq.shape[0]
# reduce number of projections in the sinogram
sino = sino_hq[::skip_theta]
# angles views in radians
angles = np.linspace(0, 2 * np.pi, na, False)[::skip_theta]
# ground truth reconstruction
true = ntp.reconstruct(sino_hq,
np.linspace(0,
np.pi * 2,
sino_hq.shape[0],
endpoint=False),
'SIRT_CUDA', {'iterations': 200},
pixel_size=pixel_size)
# fbp reconstruction
fbp = ntp.reconstruct(sino, angles, 'FBP_CUDA', pixel_size=pixel_size)
# sirt reconstruction
sirt = ntp.reconstruct(sino,
angles,
'SIRT_CUDA',
parameters={'iterations': 200},
pixel_size=pixel_size)
# cgls reconstruction
cgls = ntp.reconstruct(sino,
angles,
'CGLS_CUDA',
# remove stripes in sinograms
norm = ntp.remove_stripe_stack(norm,
level=4,
wname='db30',
sigma=1.5,
out=norm)
# define the array of the angle views in radians
angles = np.linspace(0, last_angle, norm.shape[0], endpoint=False)
# FBP reconstruction with the hamming filter using GPU
print('> Reconstruction...')
rec = ntp.reconstruct(norm,
angles,
'FBP_CUDA',
parameters={"FilterType": "hamming"},
pixel_size=pixel_size)
# Implemented FilterType in ASTRA toolbox are:
# ``ram-lak`` (default), ``shepp-logan``, ``cosine``, ``hamming``, ``hann``, ``none``, ``tukey``,
# ``lanczos``, ``triangular``, ``gaussian``, ``barlett-hann``, ``blackman``, ``nuttall``,
# ``blackman-harris``, ``blackman-nuttall``, ``flat-top``, ``kaiser``,
# ``parzen``, ``projection``, ``sinogram``, ``rprojection``, ``rsinogram``.
# select the directory and the prefix file name of the reconstructed images to save.
recon_dir = ntp.save_filename_gui(
'',
message=
'Select the folder and the prefix name for the reconstructed images...')
dose_draw=True,
crop_draw=True,
log=True)
# rotation axis tilt correction
norm = ntp.correction_COR(norm, norm[0], norm_180)
# define the array of the angle views in radians
angles = np.linspace(0, last_angle, norm.shape[0], endpoint=False)
# high-quality reconstruction
train_slice_start = 100
train_slice_end = 120
rec = ntp.reconstruct(norm[:, train_slice_start:train_slice_end + 1, :],
angles,
'FBP_CUDA',
parameters={"FilterType": "hamming"},
pixel_size=pixel_size)
# write the high-quality reconstructed images to disk
try:
os.mkdir(hqrec_folder)
except OSError:
pass
ntp.write_tiff_stack(hqrec_folder + 'sample', rec)
# NN-FBP training
skip = 3 # reduction factor of the full dataset to obtain the sparse-view dataset
norm_train = norm[::skip, train_slice_start:train_slice_end + 1, :]
ntp.reconstruct(norm_train, angles[::skip], 'NN-FBP-train', parameters=conf)
# perform minus-log transform
norm = ntp.log_transform(norm, out=norm)
# remove stripes in sinograms
norm = ntp.remove_stripe_stack(norm,
level=4,
wname='db30',
sigma=1.5,
out=norm)
# define the array of the angle views in radians
angles = np.linspace(0, last_angle, norm.shape[0], endpoint=False)
# SIRT reconstruction with 100 iterations using GPU
print('> Reconstruction...')
rec = ntp.reconstruct(norm,
angles,
'SIRT_CUDA',
parameters={"iterations": 100},
pixel_size=pixel_size)
# select the directory and the prefix file name of the reconstructed images to save.
recon_dir = ntp.save_filename_gui(
'',
message=
'Select the folder and the prefix name for the reconstructed images...')
# write the reconstructed images to disk
ntp.write_tiff_stack(recon_dir, rec)
radius=3,
threshold=0.018,
outliers='bright',
out=norm)
# perform minus-log transform
norm = ntp.log_transform(norm, out=norm)
# remove stripes in sinograms
norm = ntp.remove_stripe_stack(norm,
level=4,
wname='db30',
sigma=1.5,
out=norm)
# define the array of the angle views in radians
angles = np.linspace(0, last_angle, norm.shape[0], endpoint=False)
# FBP reconstruction using CPU
print('> Reconstruction...')
rec = ntp.reconstruct(norm, angles, 'FBP', pixel_size=pixel_size)
# select the directory and the prefix file name of the reconstructed images to save.
recon_dir = ntp.save_filename_gui(
'',
message=
'Select the folder and the prefix name for the reconstructed images...')
# write the reconstructed images to disk
ntp.write_tiff_stack(recon_dir, rec)