)
return self.res
self.res -= alpha * self.__p__
self.reinitialise_cgls()
self.re_init_at_iteration = i
self.__r__ -= alpha * q
s = tigre.Atb(self.__r__,
self.geo,
self.angles,
gpuids=self.gpuids)
s_norm = np.linalg.norm(s)
gamma1 = s_norm * s_norm
beta = gamma1 / self.__gamma__
if self.log_parameters:
self.parameter_history["alpha"][i] = alpha
self.parameter_history["beta"][i] = beta
self.parameter_history["gamma"][i] = self.__gamma__
self.parameter_history["q_norm"][i] = q_norm
self.parameter_history["s_norm"][i] = s_norm
self.__gamma__ = gamma1
self.__p__ = s + beta * self.__p__
if self.verbose:
print("Average time taken for each iteration for CGLS:" +
str(sum(avgtime) / len(avgtime)) + "(s)")
cgls = decorator(CGLS, name="cgls")
alpha = self._gamma / (q_norm * q_norm)
self.res += alpha * self._p
aux = self.proj - Ax(self.res, self.geo, self.angles, 'ray-voxel')
self.l2l[i] = np.linalg.norm(aux.ravel(), 2)
if i > 0 and self.l2l[i] > self.l2l[i - 1]:
print('re-initialization was called at iter:' + str(i))
self.res -= alpha * self._p
self.initialise_cgls()
self._r -= alpha * q
s = Atb(self._r, self.geo, self.angles)
s_norm = np.linalg.norm(s.ravel(), 2)
gamma1 = s_norm * s_norm
beta = gamma1 / self._gamma
if self.log_parameters:
self.parameter_history['alpha'][i] = alpha
self.parameter_history['beta'][i] = beta
self.parameter_history['gamma'][i] = self._gamma
self.parameter_history['q_norm'][i] = q_norm
self.parameter_history['s_norm'][i] = s_norm
self._gamma = gamma1
self._p = s + beta * self._p
cgls = decorator(CGLS)
class SART(IterativeReconAlg):
__doc__ = (
"SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconstruction Techique algorithm\n"
"SART(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry described in GEO, using NITER iterations. \n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=1))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
sart = decorator(SART, name='sart')
class SIRT(IterativeReconAlg):
__doc__ = (
"SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconxtruction Techique algorithm\n"
"SIRT(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry descrived in GEO, using NITER iterations.\n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if i == 1:
tic = time.clock()
print('Esitmated time until completetion (s): ' +
str((self.niter - 1) * (tic - toc)))
getattr(self, self.dataminimizing)()
x_rec_old = copy.deepcopy(x_rec)
x_rec = im3ddenoise(self.res, self.__numiter_tv__, 1. / lambdaForTv)
t_old = t
t = (1 + np.sqrt(1 + 4 * t ** 2)) / 2
self.res = x_rec + (t_old - 1) / t * (x_rec - x_rec_old)
self.error_measurement(res_prev, i)
fista = decorator(FISTA, name='FISTA')
class ISTA(FISTA):
__doc__ = FISTA.__doc__
def __int__(self, proj, geo, angles, niter, **kwargs):
FISTA.__init__(self, proj, geo, angles, niter, **kwargs)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
lambdaForTv = 2 * self.__bm__ * self.lmbda
class ASD_POCS(initASD_POCS):
__doc__ = initASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
if "blocksize" in kwargs and kwargs['blocksize']>1:
print('Warning: blocksize is set to 1, please use an OS version of the algorithm for blocksize > 1')
kwargs.update(dict(blocksize=1))
kwargs.update(dict(regularisation="minimizeTV"))
initASD_POCS.__init__(self, proj, geo, angles, niter, **kwargs)
asd_pocs = decorator(ASD_POCS, name="asd_pocs")
class AwASD_POCS(initASD_POCS):
__doc__ = (" AwASD_POCS is the Adaptive Weighted TV (edge preserving) version of ASD_POCS\n\n"
" :extra kwargs delta: (float)\n"
" Control amount of smoothing at edges of the image\n"
" Default delta = -0.005\n") + initASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
if "blocksize" in kwargs and kwargs['blocksize']>1:
print('Warning: blocksize is set to 1, please use an OS version of the algorithm for blocksize > 1')
kwargs.update(dict(blocksize=1))
kwargs.update(dict(regularisation="minimizeAwTV"))
self.delta = np.float32(-0.005) if "delta" not in kwargs else kwargs["delta"]
# tic = time.process_time()
den = Ax(self.res,
self.geo,
self.angles,
"interpolated",
gpuids=self.gpuids)
# toc = time.process_time()
# print('Ax time: {}'.format(toc-tic))
den[den == 0.0] = np.inf
auxmlem = self.proj / den
# auxmlem[auxmlem == np.nan] = 0.
# auxmlem[auxmlem == np.inf] = 0.
# update
# tic = time.process_time()
img = Atb(auxmlem,
self.geo,
self.angles,
backprojection_type="matched",
gpuids=self.gpuids) / self.W
# toc = time.process_time()
# print('Atb time: {}'.format(toc-tic))
# img[img == np.nan] = 0.
# img[img == np.inf] = 0.
self.res = self.res * img
self.res[self.res < 0.0] = 0.0
mlem = decorator(MLEM, name="mlem")
for item in self.__dict__:
if type(getattr(self, item)) == np.ndarray:
if np.isnan(getattr(self, item)).any():
raise ValueError('nan found for ' + item + ' at iteraton ' + str(i))
aux = self.proj - Ax(self.res, self.geo, self.angles, 'ray-voxel')
self.l2l[i] = np.linalg.norm(aux.ravel(), 2)
if i > 0 and self.l2l[i] > self.l2l[i - 1]:
print('re-initialization was called at iter:' + str(i))
self.res -= alpha * self.__p__
self.reinitialise_cgls()
self.__r__ -= alpha * q
s = Atb(self.__r__, self.geo, self.angles)
s_norm = np.linalg.norm(s.ravel(), 2)
gamma1 = s_norm * s_norm
beta = gamma1 / self.__gamma__
if self.log_parameters:
self.parameter_history['alpha'][i] = alpha
self.parameter_history['beta'][i] = beta
self.parameter_history['gamma'][i] = self.__gamma__
self.parameter_history['q_norm'][i] = q_norm
self.parameter_history['s_norm'][i] = s_norm
self.__gamma__ = gamma1
self.__p__ = s + beta * self.__p__
cgls = decorator(CGLS, name='cgls')
from tigre.algorithms.iterative_recon_alg import decorator
class SART(IterativeReconAlg):
__doc__ = ("SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconstruction Techique algorithm\n"
"SART(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry described in GEO, using NITER iterations. \n") + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=1))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
sart = decorator(SART, name='sart')
class SIRT(IterativeReconAlg):
__doc__ = ("SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconxtruction Techique algorithm\n"
"SIRT(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry descrived in GEO, using NITER iterations.\n") + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
class SART(IterativeReconAlg):
__doc__ = (
"SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconstruction Techique algorithm\n"
"SART(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry described in GEO, using NITER iterations. \n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=1))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
sart = decorator(SART, name='sart')
class SIRT(IterativeReconAlg):
__doc__ = (
"SIRT_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconxtruction Techique algorithm\n"
"SIRT(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry descrived in GEO, using NITER iterations.\n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
c = np.dot(dg_vec.reshape(-1,), dp_vec.reshape(-1,)) / max(
dg * dp, 1e-6
) # reshape ensures no copy is made.
if (c < -0.99 and dd <= self.epsilon) or self.beta < 0.005 or n_iter > self.niter:
if self.verbose:
print(
"\n"
" Stop criteria met: \n"
" c = " + str(c) + "\n"
" beta = " + str(self.beta) + "\n"
" iter = " + str(n_iter) + "\n"
)
stop_criteria = True
asd_pocs = decorator(ASD_POCS, name="asd_pocs")
class AwASD_POCS(ASD_POCS): # noqa: D101, N801
__doc__ = ASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(regularisation="minimizeAwTV"))
if "blocksize" not in kwargs:
kwargs.update(dict(blocksize=1))
else:
self.blocksize = 1
if "delta" not in kwargs:
self.delta = np.array([-0.005], dtype=np.float32)[0]
ASD_POCS.__init__(self, proj, geo, angles, niter, **kwargs)
'Esitmated time until completetion (s): {:.4f}'.format(
(self.niter - 1) * (tic - toc)))
# tic = time.process_time()
den = Ax(self.res,
self.geo,
self.angles,
'interpolated',
gpuids=self.gpuids)
# toc = time.process_time()
# print('Ax time: {}'.format(toc-tic))
den[den == 0.] = np.inf
auxmlem = self.proj / den
# auxmlem[auxmlem == np.nan] = 0.
# auxmlem[auxmlem == np.inf] = 0.
# update
# tic = time.process_time()
img = Atb(auxmlem, self.geo, self.angles,
gpuids=self.gpuids) / self.W
# toc = time.process_time()
# print('Atb time: {}'.format(toc-tic))
# img[img == np.nan] = 0.
# img[img == np.inf] = 0.
self.res = self.res * img
self.res[self.res < 0.] = 0.
mlem = decorator(MLEM, name='mlem')
class SART(IterativeReconAlg): # noqa: D101
__doc__ = (
"SART_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconstruction Techique algorithm\n"
"SART(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry described in GEO, using NITER iterations. \n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=1))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
sart = decorator(SART, name="sart")
class SIRT(IterativeReconAlg): # noqa: D101
__doc__ = (
"SIRT_CBCT solves Cone Beam CT image reconstruction using Oriented Subsets\n"
"Simultaneous Algebraic Reconxtruction Techique algorithm\n"
"SIRT(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem\n"
"using the projection data PROJ taken over ALPHA angles, corresponding\n"
"to the geometry descrived in GEO, using NITER iterations.\n"
) + IterativeReconAlg.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if dg > self.rmax*dp and dd > self.epsilon:
dtvg = dtvg*self.alpha_red
self.beta *= self.beta_red
c = np.dot(dg_vec.reshape(-1,), dp_vec.reshape(-1,))/max(dg*dp, 1e-6)
if (c < -0.99 and dd <= self.epsilon) or self.beta < 0.005 or n_iter > self.niter:
if self.verbose:
print("\n"
" Stop criteria met: \n"
" c = " + str(c) + "\n"
" beta = " + str(self.beta) + "\n"
" iter = " + str(n_iter)) + "\n"
stop_criteria = True
asd_pocs = decorator(ASD_POCS, name='asd_pocs')
class AwASD_POCS(ASD_POCS):
__doc__ = ASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(regularisation='minimizeAwTV'))
if 'delta' not in kwargs:
self.delta = np.array([-0.005], dtype=np.float32)[0]
ASD_POCS.__init__(self, proj, geo, angles, niter, **kwargs)
awasd_pocs = decorator(AwASD_POCS, name='awasd_pocs')
)
return self.res
self.res -= alpha * self.__p__
self.reinitialise_cgls()
self.re_init_at_iteration = i
self.__r__ -= alpha * q
s = tigre.Atb(self.__r__,
self.geo,
self.angles,
gpuids=self.gpuids)
s_norm = np.linalg.norm(s)
gamma1 = s_norm * s_norm
beta = gamma1 / self.__gamma__
if self.log_parameters:
self.parameter_history['alpha'][i] = alpha
self.parameter_history['beta'][i] = beta
self.parameter_history['gamma'][i] = self.__gamma__
self.parameter_history['q_norm'][i] = q_norm
self.parameter_history['s_norm'][i] = s_norm
self.__gamma__ = gamma1
self.__p__ = s + beta * self.__p__
if self.verbose:
print('Average time taken for each iteration for CGLS:' +
str(sum(avgtime) / len(avgtime)) + '(s)')
cgls = decorator(CGLS, name='cgls')
tic = default_timer()
print("Esitmated time until completetion (s): " +
str((self.niter - 1) * (tic - toc)))
getattr(self, self.dataminimizing)()
x_rec_old = copy.deepcopy(x_rec)
x_rec = im3ddenoise(self.res, self.__numiter_tv__,
1.0 / lambdaForTv, self.gpuids)
t_old = t
t = (1 + np.sqrt(1 + 4 * t**2)) / 2
self.res = x_rec + (t_old - 1) / t * (x_rec - x_rec_old)
self.error_measurement(res_prev, i)
fista = decorator(FISTA, name="FISTA")
class ISTA(FISTA): # noqa: D101
__doc__ = FISTA.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
FISTA.__init__(self, proj, geo, angles, niter, **kwargs)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
lambdaForTv = 2 * self.__bm__ * self.lmbda
self.beta *= self.beta_red
c = np.dot(dg_vec.reshape(-1, ), dp_vec.reshape(-1, )) / max(
dg * dp, 1e-6)
if (c < -0.99 and dd <= self.epsilon
) or self.beta < 0.005 or n_iter > self.niter:
if self.verbose:
print("\n"
" Stop criteria met: \n"
" c = " + str(c) + "\n"
" beta = " + str(self.beta) + "\n"
" iter = " + str(n_iter)) + "\n"
stop_criteria = True
asd_pocs = decorator(ASD_POCS, name='asd_pocs')
class AwASD_POCS(ASD_POCS):
__doc__ = ASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(regularisation='minimizeAwTV'))
if 'delta' not in kwargs:
self.delta = np.array([-0.005], dtype=np.float32)[0]
ASD_POCS.__init__(self, proj, geo, angles, niter, **kwargs)
awasd_pocs = decorator(AwASD_POCS, name='awasd_pocs')
c = np.dot(dg_vec.reshape(-1,), dp_vec.reshape(-1,)) / max(
dg * dp, 1e-6
) # reshape ensures no copy is made.
if (c < -0.99 and dd <= self.epsilon) or self.beta < 0.005 or n_iter > self.niter:
if self.verbose:
print(
"\n"
" Stop criteria met: \n"
" c = " + str(c) + "\n"
" beta = " + str(self.beta) + "\n"
" iter = " + str(n_iter) + "\n"
)
stop_criteria = True
asd_pocs = decorator(ASD_POCS, name="asd_pocs")
class AwASD_POCS(ASD_POCS): # noqa: D101, N801
__doc__ = ASD_POCS.__doc__
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(regularisation="minimizeAwTV"))
if "delta" not in kwargs:
self.delta = np.array([-0.005], dtype=np.float32)[0]
ASD_POCS.__init__(self, proj, geo, angles, niter, **kwargs)
awasd_pocs = decorator(AwASD_POCS, name="awasd_pocs")
