det_num,det_w)
elif test_case==2:
angles = np.linspace(0,2*np.pi,angles_num,endpoint=False)
ag = AcquisitionGeometry('cone',
'2D',
angles,
det_num,
det_w,
dist_source_center=SourceOrig,
dist_center_detector=OrigDetec)
else:
NotImplemented
# Set up Operator object combining the ImageGeometry and AcquisitionGeometry
# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU.
Aop = AstraProjectorSimple(ig, ag, 'gpu')
# Forward and backprojection are available as methods direct and adjoint. Here
# generate test data b and do simple backprojection to obtain z.
b = Aop.direct(Phantom)
z = Aop.adjoint(b)
plt.imshow(b.array)
plt.title('Simulated data')
plt.colorbar()
plt.show()
plt.imshow(z.array)
plt.title('Backprojected data')
plt.colorbar()
plt.show()
ag = AcquisitionGeometry('parallel', '2D', angles, det_num, det_w)
elif test_case == 2:
angles = np.linspace(0, 2 * np.pi, angles_num, endpoint=False)
ag = AcquisitionGeometry('cone',
'2D',
angles,
det_num,
det_w,
dist_source_center=SourceOrig,
dist_center_detector=OrigDetec)
else:
NotImplemented
# Set up Operator object combining the ImageGeometry and AcquisitionGeometry
# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU.
Aop = AstraProjectorSimple(ig, ag, 'gpu')
# Forward and backprojection are available as methods direct and adjoint. Here
# generate test data b and do simple backprojection to obtain z.
b = Aop.direct(Phantom)
z = Aop.adjoint(b)
plt.imshow(b.array)
plt.title('Simulated data')
plt.show()
plt.imshow(z.array)
plt.title('Backprojected data')
plt.show()
# Using the test data b, different reconstruction methods can now be set up as
# Reduce to single (noncentral) slice by extracting relevant parameters from data and its
# geometry. Perhaps create function to extract central slice automatically?
b2d = b.subset(vertical=128)
ag2d = AcquisitionGeometry('parallel',
'2D',
ag.angles,
pixel_num_h=ag.pixel_num_h)
b2d.geometry = ag2d
# Create 2D image geometry
ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h,
voxel_num_y=ag2d.pixel_num_h)
# Create GPU projector/backprojector operator with ASTRA.
Aop = AstraProjectorSimple(ig2d,ag2d,'gpu')
# Demonstrate operator is working by applying simple backprojection.
z = Aop.adjoint(b2d)
plt.imshow(z.array)
plt.title('Simple backprojection')
plt.colorbar()
plt.show()
# Set initial guess ImageData with zeros for algorithms, and algorithm options.
x_init = ImageData(numpy.zeros((imsize,imsize)),
geometry=ig2d)
opt_CGLS = {'tol': 1e-4, 'iter': 20}
# Run CGLS algorithm and display reconstruction.
x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b2d, opt_CGLS)
eqzero = ProjAngleChannels == 0
ProjAngleChannels[eqzero] = 1
ProjAngleChannels_NormLog = -np.log(
ProjAngleChannels) # normalised and neg-log data
# extact sinogram over energy channels
selectedVertical_slice = 256
sino_all_channels = ProjAngleChannels_NormLog[:, :, :, selectedVertical_slice]
# Set angles to use
angles = np.linspace(-np.pi, np.pi, totalAngles, endpoint=False)
# set the geometry
ig = ImageGeometry(n, n)
ag = AcquisitionGeometry('parallel', '2D', angles, n, 1)
Aop = AstraProjectorSimple(ig, ag, 'gpu')
# loop to reconstruct energy channels
REC_chan = np.zeros((totChannels, n, n), 'float32')
for i in range(0, totChannels, 1):
sino_channel = sino_all_channels[:,
i, :] # extract a sinogram for i-th channel
print("Initial guess")
x_init = ImageData(geometry=ig)
# Create least squares object instance with projector and data.
print("Create least squares object instance with projector and data.")
f = Norm2sq(Aop, DataContainer(sino_channel), c=0.5)
print("Run FISTA-TV for least squares")
# Choose the number of voxels to reconstruct onto as number of detector pixels
N = data.geometry.pixel_num_h
# Geometric magnification
mag = (np.abs(data.geometry.dist_center_detector) + \
np.abs(data.geometry.dist_source_center)) / \
np.abs(data.geometry.dist_source_center)
# Voxel size is detector pixel size divided by mag
voxel_size_h = data.geometry.pixel_size_h / mag
# Construct the appropriate ImageGeometry
ig2d = ImageGeometry(voxel_num_x=N,
voxel_num_y=N,
voxel_size_x=voxel_size_h,
voxel_size_y=voxel_size_h)
# Set up the Projector (AcquisitionModel) using ASTRA on GPU
Aop = AstraProjectorSimple(ig2d, ag2d, "gpu")
# Set initial guess for CGLS reconstruction
x_init = ImageData(np.zeros((N, N)), geometry=ig2d)
# Run 50-iteration CGLS reconstruction
opt = {'tol': 1e-4, 'iter': 50}
x, it, timing, criter = CGLS(x_init, Aop, data2d, opt=opt)
# Display reconstruction
plt.figure()
plt.imshow(x.as_array())
plt.colorbar()