def fbp(proj, geo, angles, filter=None, verbose=False, **kwargs):
__doc__ = FDK.__doc__
if geo.mode != 'parallel':
raise ValueError("Only use FBP for parallel beam. Check geo.mode.")
geox = copy.deepcopy(geo)
geox.check_geo(angles)
proj_filt = filtering(copy.deepcopy(proj), geox, angles, parker=False, verbose=verbose)
res = Atb(proj_filt, geo, angles) * geo.DSO / geo.DSD
return res
def fbp(proj, geo, angles, **kwargs):
__doc__ = FDK.__doc__
if geo.mode != 'parallel':
raise ValueError("Only use FBP for parallel beam. Check geo.mode.")
geox = copy.deepcopy(geo)
geox.check_geo(angles)
if 'verbose' in kwargs:
verbose = kwargs['verbose']
else:
verbose = False
proj_filt = filtering(copy.deepcopy(proj),
geox,
angles,
parker=False,
verbose=verbose)
res = Atb(proj_filt, geo, angles) * geo.DSO / geo.DSD
return res
def fbp(proj, geo, angles, **kwargs): # noqa: D103
__doc__ = FDK.__doc__ # noqa: F841
if geo.mode != "parallel":
raise ValueError("Only use FBP for parallel beam. Check geo.mode.")
geox = copy.deepcopy(geo)
geox.check_geo(angles)
if "verbose" in kwargs:
verbose = kwargs["verbose"]
else:
verbose = False
if "gpuids" in kwargs:
gpuids = kwargs["gpuids"]
else:
gpuids = None
proj_filt = filtering(copy.deepcopy(proj),
geox,
angles,
parker=False,
verbose=verbose)
res = Atb(proj_filt, geo, angles, gpuids=gpuids) * geo.DSO / geo.DSD
return res
def FDK(proj, geo, angles, **kwargs):
"""
solves CT image reconstruction.
:param proj: np.array(dtype=float32),
Data input in the form of 3d
:param geo: tigre.utilities.geometry.Geometry
Geometry of detector and image (see examples/Demo code)
:param angles: np.array(dtype=float32)
Angles of projection, shape = (nangles,3) or (nangles,)
:param filter: str
Type of filter used for backprojection
opts: "shep_logan"
"cosine"
"hamming"
"hann"
:param verbose: bool
Feedback print statements for algorithm progress
:param kwargs: dict
keyword arguments
:return: np.array(dtype=float32)
Usage:
-------
>>> import tigre
>>> import tigre.algorithms as algs
>>> import numpy
>>> from tigre.demos.Test_data import data_loader
>>> geo = tigre.geometry(mode='cone',default_geo=True,
>>> nVoxel=np.array([64,64,64]))
>>> angles = np.linspace(0,2*np.pi,100)
>>> src_img = data_loader.load_head_phantom(geo.nVoxel)
>>> proj = tigre.Ax(src_img,geo,angles)
>>> output = algs.FDK(proj,geo,angles)
tigre.demos.run() to launch ipython notebook file with examples.
--------------------------------------------------------------------
This file is part of the TIGRE Toolbox
Copyright (c) 2015, University of Bath and
CERN-European Organization for Nuclear Research
All rights reserved.
License: Open Source under BSD.
See the full license at
https://github.com/CERN/TIGRE/license.txt
Contact: [email protected]
Codes: https://github.com/CERN/TIGRE/
--------------------------------------------------------------------
Coded by: MATLAB (original code): Ander Biguri
PYTHON : Reuben Lindroos
"""
if "niter" in kwargs:
kwargs.pop("niter")
if "verbose" in kwargs:
verbose = kwargs["verbose"]
else:
verbose = False
if "gpuids" in kwargs:
gpuids = kwargs["gpuids"]
else:
gpuids = None
geo = copy.deepcopy(geo)
geo.check_geo(angles)
geo.checknans()
if "filter" in kwargs:
filter = kwargs["filter"]
else:
filter = None
if filter is not None:
geo.filter = kwargs["filter"]
# Weight
proj_filt = np.zeros(proj.shape, dtype=np.float32)
for ii in range(angles.shape[0]):
xv = (np.arange((-geo.nDetector[1] / 2) + 0.5, 1 +
(geo.nDetector[1] / 2) - 0.5) * geo.dDetector[1])
yv = (np.arange((-geo.nDetector[0] / 2) + 0.5, 1 +
(geo.nDetector[0] / 2) - 0.5) * geo.dDetector[0])
(yy, xx) = np.meshgrid(xv, yv)
w = geo.DSD[0] / np.sqrt((geo.DSD[0]**2 + xx**2 + yy**2))
proj_filt[ii] = copy.deepcopy(proj[ii]) * w
proj_filt = filtering(proj_filt,
geo,
angles,
parker=False,
verbose=verbose)
# m = {
# 'py_projfilt': proj_filt,
#
# }
# scipy.io.savemat('Tests/Filter_data', m)
res = Atb(proj_filt, geo, geo.angles, "FDK", gpuids=gpuids)
# res = 0
# res = Atb(proj,geo,angles,'FDK')
return res
def FDK(proj, geo, angles, filter=None,verbose=False):
('\n'
'FDK solves Cone Beam CT image reconstruction'
'\n'
'Parameters \n'
'-------------------------------------------------------------------\n'
'\n'
'proj: Data input in the form of 3d, np.array(dtype=float32)\n'
'\n'
'geo: Geometry of detector and image (see examples/Demo code)\n'
'\n'
'alpha: 1d array of angles corresponding to image data, np.array(dtype=float32)\n'
'\n'
'filter: default="ram_lak" \n'
' opts: \n'
' "shep_logan"'
' "cosine" '
' "hamming" '
' "hann" '
'Examples \n'
'---------------------------------------------------------------------------\n'
'See Demos/Example code for further instructions.\n'
'---------------------------------------------------------------------------'
'\n'
"""This file is part of the TIGRE Toolbox
Copyright (c) 2015, University of Bath and
CERN-European Organization for Nuclear Research
All rights reserved.
License: Open Source under BSD.
See the full license at
https://github.com/CERN/TIGRE/license.txt
Contact: [email protected]
Codes: https://github.com/CERN/TIGRE/
--------------------------------------------------------------------------
Coded by: MATLAB (original code): Ander Biguri
PYTHON : Reuben Lindroos,Sam Loescher, """)
geo = copy.deepcopy(geo)
geo.check_geo(angles)
if filter is not None:
geo.filter = filter
# Weight
proj_filt = np.zeros(proj.shape, dtype=np.float32)
for ii in range(angles.shape[0]):
xv = np.arange((-geo.nDetector[1] / 2) + 0.5, 1 + (geo.nDetector[1] / 2) - 0.5) * geo.dDetector[1]
yv = np.arange((-geo.nDetector[0] / 2) + 0.5, 1 + (geo.nDetector[0] / 2) - 0.5) * geo.dDetector[0]
(xx, yy) = np.meshgrid(xv, yv)
w = geo.DSD[0] / np.sqrt((geo.DSD[0] ** 2 + xx ** 2 + yy ** 2))
proj_filt[ii] = proj[ii] * w
proj_filt = filtering(proj_filt, geo, angles, parker=False,verbose=verbose)
# m = {
# 'py_projfilt': proj_filt,
#
# }
# scipy.io.savemat('Tests/Filter_data', m)
res = Atb(proj_filt, geo, geo.angles, 'FDK')
# res = 0
# res = Atb(proj,geo,angles,'FDK')
return res
def FDK(proj, geo, angles, filter='ram_lak', verbose=False, **kwargs):
"""
solves CT image reconstruction.
:param proj: np.array(dtype=float32),
Data input in the form of 3d
:param geo: tigre.utilities.geometry.Geometry
Geometry of detector and image (see examples/Demo code)
:param angles: np.array(dtype=float32)
Angles of projection, shape = (nangles,3) or (nangles,)
:param filter: str
Type of filter used for backprojection
opts: "shep_logan"
"cosine"
"hamming"
"hann"
:param verbose: bool
Feedback print statements for algorithm progress
:param kwargs: dict
keyword arguments
:return: np.array(dtype=float32)
Usage:
-------
>>> import tigre
>>> import tigre.algorithms as algs
>>> import numpy
>>> from tigre.demos.Test_data import data_loader
>>> geo = tigre.geometry(mode='cone',default_geo=True,
>>> nVoxel=np.array([64,64,64]))
>>> angles = np.linspace(0,2*np.pi,100)
>>> src_img = data_loader.load_head_phantom(geo.nVoxel)
>>> proj = tigre.Ax(src_img,geo,angles)
>>> output = algs.FDK(proj,geo,angles)
tigre.demos.run() to launch ipython notebook file with examples.
--------------------------------------------------------------------
This file is part of the TIGRE Toolbox
Copyright (c) 2015, University of Bath and
CERN-European Organization for Nuclear Research
All rights reserved.
License: Open Source under BSD.
See the full license at
https://github.com/CERN/TIGRE/license.txt
Contact: [email protected]
Codes: https://github.com/CERN/TIGRE/
----------------------------------------------------------------------
Coded by: MATLAB (original code): Ander Biguri
PYTHON : Reuben Lindroos
"""
if 'niter' in kwargs:
kwargs.pop('niter')
geo = copy.deepcopy(geo)
geo.check_geo(angles)
geo.checknans()
if filter is not None:
geo.filter = filter
# Weight
proj_filt = np.zeros(proj.shape, dtype=np.float32)
for ii in range(angles.shape[0]):
xv = np.arange((-geo.nDetector[1] / 2) + 0.5, 1 + (geo.nDetector[1] / 2) - 0.5) * geo.dDetector[1]
yv = np.arange((-geo.nDetector[0] / 2) + 0.5, 1 + (geo.nDetector[0] / 2) - 0.5) * geo.dDetector[0]
(yy, xx) = np.meshgrid(xv, yv)
w = geo.DSD[0] / np.sqrt((geo.DSD[0] ** 2 + xx ** 2 + yy ** 2))
proj_filt[ii] = copy.deepcopy(proj[ii]) * w
proj_filt = filtering(proj_filt, geo, angles, parker=False, verbose=verbose)
# m = {
# 'py_projfilt': proj_filt,
#
# }
# scipy.io.savemat('Tests/Filter_data', m)
res = Atb(proj_filt, geo, geo.angles, 'FDK')
# res = 0
# res = Atb(proj,geo,angles,'FDK')
return res