scanner = ppp.RegularPolygonPETScanner(ncrystals_per_module=np.array([16, 1]),
nmodules=np.array([28, 1]))
# setup a test image
voxsize = np.array([2., 2., 2.])
n0 = 120
n1 = 120
n2 = max(1, int((scanner.xc2.max() - scanner.xc2.min()) / voxsize[2]))
# setup a random image
img = np.zeros((n0, n1, n2), dtype=np.float32)
img[(n0 // 4):(3 * n0 // 4), (n1 // 4):(3 * n1 // 4), :] = 1
img_origin = (-(np.array(img.shape) / 2) + 0.5) * voxsize
# generate sinogram parameters and the projector
sino_params = ppp.PETSinogramParameters(scanner, ntofbins=17, tofbin_width=15.)
proj = ppp.SinogramProjector(scanner,
sino_params,
img.shape,
nsubsets=1,
voxsize=voxsize,
img_origin=img_origin,
ngpus=ngpus,
tof=True,
sigma_tof=60. / 2.35,
n_sigmas=3.)
img_fwd = proj.fwd_project(img, subset=0)
# generate sensitity sinogram (product from attenuation and normalization sinogram)
# this sinogram is usually non TOF! which results in a different shape
# setup a scanner
scanner = ppp.RegularPolygonPETScanner(ncrystals_per_module=np.array([16, 1]),
nmodules=np.array([28, 1]))
# setup a test image
voxsize = np.array([2., 2., 2.])
n0 = 120
n1 = 120
n2 = max(1, int((scanner.xc2.max() - scanner.xc2.min()) / voxsize[2]))
# setup a random image
img = np.random.rand(n0, n1, n2)
img_origin = (-(np.array(img.shape) / 2) + 0.5) * voxsize
######## nontof projections
sino_params = ppp.PETSinogramParameters(scanner)
proj = ppp.SinogramProjector(scanner,
sino_params,
img.shape,
nsubsets=nsubsets,
voxsize=voxsize,
img_origin=img_origin,
ngpus=ngpus)
# setup a random sinogram
rsino = np.random.rand(*proj.subset_sino_shapes[subset])
img_fwd = proj.fwd_project(img, subset=subset)
back = proj.back_project(rsino, subset=subset)
# check if fwd and back projection are adjoint
for i2 in range(n2):
img[:, :, i2] = tmp
elif phantom == 'brain2d':
n = 128
img = np.zeros((n, n, n2), dtype=np.float32)
tmp = brain2d_phantom(n=n)
for i2 in range(n2):
img[:, :, i2] = tmp
img_origin = (-(np.array(img.shape) / 2) + 0.5) * voxsize
# setup an attenuation image
att_img = (img > 0) * 0.01 * voxsize[0]
# generate nonTOF sinogram parameters and the nonTOF projector for attenuation projection
sino_params = ppp.PETSinogramParameters(scanner, rtrim=146)
proj = ppp.SinogramProjector(scanner,
sino_params,
img.shape,
nsubsets=1,
voxsize=voxsize,
img_origin=img_origin,
ngpus=ngpus)
attn_sino = np.exp(-proj.fwd_project(att_img))
# generate the sensitivity sinogram
sens_sino = np.ones(sino_params.shape, dtype=np.float32)
# forward project the image
img_fwd = ppp.pet_fwd_model(img, proj, attn_sino, sens_sino, 0, fwhm=fwhm_data)
nmodules = np.array([28,5]))
# setup a test image
voxsize = np.array([2.,2.,2.])
n0 = 250
n1 = 250
n2 = max(1,int((scanner.xc2.max() - scanner.xc2.min()) / voxsize[2]))
# setup a random image
img = np.zeros((n0,n1,n2), dtype = np.float32, order = img_mem_order)
img[(n0//6):(5*n0//6),(n1//6):(5*n1//6),:] = 1
img_origin = (-(np.array(img.shape) / 2) + 0.5) * voxsize
# generate sinogram parameters and the projector
sino_params = ppp.PETSinogramParameters(scanner, ntofbins = ntofbins, tofbin_width = 23.,
spatial_dim_order = spatial_dim_order)
proj = ppp.SinogramProjector(scanner, sino_params, img.shape, nsubsets = nsubsets,
voxsize = voxsize, img_origin = img_origin, ngpus = ngpus,
tof = tof, sigma_tof = 60./2.35, n_sigmas = 3.,
threadsperblock = tpb)
# contamination sinogram with scatter and randoms
# useful to avoid division by 0 in the ratio of data and exprected data
ones_sino = np.ones(proj.subset_sino_shapes[0], dtype = np.float32)
#-------------------------------------------------------------------------------------
# time sino fwd and back projection
# do a forward / back projection of subset 0 - same as img_fwd = proj.fwd_project(img, 0)
# we just write out the single steps to time the python overhead separately
t_sino_fwd = np.zeros(n)