def __init__(self, proj, geo, angles, niter, **kwargs):
if "blocksize" in kwargs and kwargs['blocksize'] > 1:
print(
'Warning: blocksize is set to {}, please do not specify blocksize for this algorithm'
.format(angles.shape[0]))
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
if "blocksize" not in kwargs:
kwargs.update(blocksize=1)
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if "alpha" not in kwargs:
self.alpha = 0.002
if "alpha_red" not in kwargs:
self.alpha_red = 0.95
if "rmax" not in kwargs:
self.rmax = 0.95
if "maxl2err" not in kwargs:
self.epsilon = (
im3DNORM(Ax(FDK(proj, geo, angles, gpuids=self.gpuids), geo, angles) - proj, 2)
* 0.2
)
else:
self.epsilon = kwargs["maxl2err"]
if "tviter" not in kwargs:
self.numiter_tv = 20
else:
self.numiter_tv = kwargs["tviter"]
if "regularisation" not in kwargs:
self.regularisation = "minimizeTV"
self.beta = self.lmbda
self.beta_red = self.lmbda_red
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))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
# Don't precompute V and W.
kwargs.update(dict(W=None, V=None))
kwargs.update(dict(blocksize=angles.shape[0]))
self.log_parameters = False
self.re_init_at_iteration = 0
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if self.log_parameters:
parameter_history = {}
iterations = self.niter
parameter_history['alpha'] = np.zeros([iterations],
dtype=np.float32)
parameter_history['beta'] = np.zeros([iterations],
dtype=np.float32)
parameter_history['gamma'] = np.zeros([iterations],
dtype=np.float32)
parameter_history['q_norm'] = np.zeros([iterations],
dtype=np.float32)
parameter_history['s_norm'] = np.zeros([iterations],
dtype=np.float32)
self.parameter_history = parameter_history
self.__r__ = self.proj - \
Ax(self.res, self.geo, self.angles, 'Siddon', gpuids = self.gpuids)
self.__p__ = Atb(self.__r__, self.geo, self.angles, gpuids=self.gpuids)
p_norm = np.linalg.norm(self.__p__.ravel(), 2)
self.__gamma__ = p_norm * p_norm
def __init__(self, proj, geo, angles, niter, **kwargs):
if 'tvlambda' not in kwargs:
kwargs.update(dict(tvlambda=50))
if 'tviter' not in kwargs:
kwargs.update(dict(tviter=50))
# these two settings work well for nVoxel=[254,254,254]
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
self.blocksize = 20 if 'blocksize' not in kwargs else kwargs[
'blocksize']
self.tvlambda = 50 if 'tvlambda' not in kwargs else kwargs['tvlambda']
self.tviter = 50 if 'tviter' not in kwargs else kwargs['tviter']
# these two settings work well for nVoxel=[254,254,254]
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
# Don't precompute V and W.
kwargs.update(dict(W=None, V=None))
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if self.init is None:
self.res += 1.
self.W = Atb(np.ones(proj.shape, dtype=np.float32), geo, angles)
self.W[self.W <= 0.] = np.inf
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))
self.tvlambda = 50 if 'tvlambda' not in kwargs else kwargs['tvlambda']
self.tviter = 50 if 'tviter' not in kwargs else kwargs['tviter']
# these two settings work well for nVoxel=[254,254,254]
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
# Don't precompute W and V
kwargs.update({"W": None, "V": None})
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
self.lmbda = 0.1
self.__L__ = 2.0e8 if "hyper" not in kwargs else kwargs["hyper"]
self.__numiter_tv__ = 20 if "tviter" not in kwargs else kwargs["tviter"]
self.__lambda__ = 0.1 if "tvlambda" not in kwargs else kwargs["tvlambda"]
self.__t__ = 1
self.__bm__ = 1.0 / self.__L__
self.__p__ = 1 if "fista_p" not in kwargs else kwargs["fista_p"]
self.__q__ = 1 if "fista_q" not in kwargs else kwargs["fista_q"]
def __init__(self, proj, geo, angles, niter, **kwargs):
# Dont precompute W and V
kwargs.update(dict(W=None,
V=None,
))
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
self.lmbda = 0.1
if 'hyper' not in kwargs:
self.__L__ = 2.e4
else:
self.__L__ = kwargs['hyper']
self.__t__ = 1
self.__bm__ = 1. / self.__L__
def __init__(self, proj, geo, angles, niter, **kwargs):
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if not kwargs.has_key('alpha'):
self.alpha = 0.002
if not kwargs.has_key('alpha_red'):
self.alpha_red = 0.95
if not kwargs.has_key('rmax'):
self.rmax = 0.95
if not kwargs.has_key('maxl2err'):
self.epsilon = im3DNORM(FDK(proj, geo, angles), 2) * 0.2
if not kwargs.has_key("numiter_tv"):
self.numiter_tv = 20
if not kwargs.has_key('regularisation'):
self.regularisation = 'minimizeTV'
self.beta = self.lmbda
self.beta_red = self.lmbda_red
def __init__(self, proj, geo, angles, niter, **kwargs):
# if "blocksize" not in kwargs:
# kwargs.update(dict(blocksize=1))
#kwargs.update(dict(regularisation="minimizeTV"))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if "maxl2err" not in kwargs:
self.epsilon = (
im3DNORM(Ax(FDK(proj, geo, angles, gpuids=self.gpuids), geo, angles) - proj, 2)
* 0.2
)
else:
self.epsilon = kwargs["maxl2err"]
self.numiter_tv = 20 if "tviter" not in kwargs else kwargs["tviter"]
self.beta = self.lmbda
self.beta_red = self.lmbda_red
def __init__(self, proj, geo, angles, niter, **kwargs):
if 'blocksize' not in kwargs:
kwargs.update(blocksize=1)
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if 'alpha' not in kwargs:
self.alpha = 0.002
if 'alpha_red' not in kwargs:
self.alpha_red = 0.95
if 'rmax' not in kwargs:
self.rmax = 0.95
if 'maxl2err' not in kwargs:
self.epsilon = im3DNORM(FDK(proj, geo, angles), 2) * 0.2
if "numiter_tv" not in kwargs:
self.numiter_tv = 20
if 'regularisation' not in kwargs:
self.regularisation = 'minimizeTV'
self.beta = self.lmbda
self.beta_red = self.lmbda_red
def __init__(self,proj,geo,angles,niter,**kwargs):
# Don't precompute V and W.
kwargs.update(dict(W=None,V=None))
kwargs.update(dict(blocksize=angles.shape[0]))
self.log_parameters = False
# Avoid typo checking
IterativeReconAlg.__init__(self,proj,geo,angles,niter,**kwargs)
self.initialise_cgls()
if self.log_parameters:
parameter_history = {}
iterations = self.niter
parameter_history['alpha'] = np.zeros([iterations], dtype=np.float32)
parameter_history['beta'] = np.zeros([iterations], dtype=np.float32)
parameter_history['gamma'] = np.zeros([iterations], dtype=np.float32)
parameter_history['q_norm'] = np.zeros([iterations], dtype=np.float32)
parameter_history['s_norm'] = np.zeros([iterations], dtype=np.float32)
self.parameter_history = parameter_history
def __init__(self, proj, geo, angles, niter, **kwargs):
if 'blocksize' not in kwargs:
kwargs.update(blocksize=1)
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if 'alpha' not in kwargs:
self.alpha = 0.002
if 'alpha_red' not in kwargs:
self.alpha_red = 0.95
if 'rmax' not in kwargs:
self.rmax = 0.95
if 'maxl2err' not in kwargs:
self.epsilon = im3DNORM(FDK(proj, geo, angles), 2)*0.2
if "numiter_tv" not in kwargs:
self.numiter_tv = 20
if 'regularisation' not in kwargs:
self.regularisation = 'minimizeTV'
self.beta = self.lmbda
self.beta_red = self.lmbda_red
def __init__(self, proj, geo, angles, niter, **kwargs):
# Don't precompute V and W.
kwargs.update(dict(W=None, V=None))
kwargs.update(dict(blocksize=angles.shape[0]))
self.log_parameters = False
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
if self.log_parameters:
parameter_history = {}
iterations = self.niter
parameter_history['alpha'] = np.zeros([iterations], dtype=np.float32)
parameter_history['beta'] = np.zeros([iterations], dtype=np.float32)
parameter_history['gamma'] = np.zeros([iterations], dtype=np.float32)
parameter_history['q_norm'] = np.zeros([iterations], dtype=np.float32)
parameter_history['s_norm'] = np.zeros([iterations], dtype=np.float32)
self.parameter_history = parameter_history
self.__r__ = self.proj - Ax(self.res, self.geo, self.angles, 'ray-voxel')
self.__p__ = Atb(self.__r__, self.geo, self.angles)
p_norm = np.linalg.norm(self.__p__.ravel(), 2)
self.__gamma__ = p_norm * p_norm
def __init__(self, proj, geo, angles, niter, **kwargs):
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
self.blocksize = 20 if 'blocksize' not in kwargs else kwargs[
"blocksize"]
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)
def __init__(self, proj, geo, angles, niter, **kwargs):
kwargs.update(dict(blocksize=angles.shape[0]))
IterativeReconAlg.__init__(self, proj, geo, angles, niter, **kwargs)