def setUp(self):
data_path = os.path.join(examples_data_path('PET'),
'thorax_single_slice')
image = pet.ImageData(os.path.join(data_path, 'emission.hv'))
am = pet.AcquisitionModelUsingRayTracingMatrix()
am.set_num_tangential_LORs(5)
templ = pet.AcquisitionData(
os.path.join(data_path, 'template_sinogram.hs'))
am.set_up(templ, image)
acquired_data = am.forward(image)
obj_fun = pet.make_Poisson_loglikelihood(acquired_data)
obj_fun.set_acquisition_model(am)
obj_fun.set_up(image)
self.obj_fun = obj_fun
self.image = image
def setUp(self):
os.chdir(examples_data_path('PET'))
#%% copy files to working folder and change directory to where the output files are
shutil.rmtree('working_folder/thorax_single_slice',True)
shutil.copytree('thorax_single_slice','working_folder/thorax_single_slice')
os.chdir('working_folder/thorax_single_slice')
image = pet.ImageData('emission.hv')
am = pet.AcquisitionModelUsingRayTracingMatrix()
am.set_num_tangential_LORs(5)
templ = pet.AcquisitionData('template_sinogram.hs')
am.set_up(templ,image)
acquired_data=am.forward(image)
obj_fun = pet.make_Poisson_loglikelihood(acquired_data)
obj_fun.set_acquisition_model(am)
obj_fun.set_up(image)
self.obj_fun = obj_fun
self.image = image
def test_main(rec=False, verb=False, throw=True):
# Set STIR verbosity to off
original_verb = pet.get_verbosity()
pet.set_verbosity(1)
time.sleep(0.5)
sys.stderr.write("Testing NiftyPET projector...")
time.sleep(0.5)
# Get image
image = get_image()
# Get AM
try:
acq_model = pet.AcquisitionModelUsingNiftyPET()
except:
return 1, 1
acq_model.set_cuda_verbosity(verb)
data_path = examples_data_path('PET')
# raw_data_path = pet.existing_filepath(os.path.join(data_path, 'mMR'), 'mMR_template_span11.hs')
raw_data_path = os.path.join(data_path, 'mMR')
template_acq_data = pet.AcquisitionData(
os.path.join(raw_data_path, 'mMR_template_span11.hs'))
acq_model.set_up(template_acq_data, image)
# Test operator adjointness
if verb:
print('testing adjointness')
if not is_operator_adjoint(acq_model, num_tests=1, verbose=True):
raise AssertionError('NiftyPet AcquisitionModel is not adjoint')
# Generate test data
simulated_acq_data = acq_model.forward(image)
simulated_acq_data_w_noise = add_noise(simulated_acq_data, 10)
obj_fun = pet.make_Poisson_loglikelihood(template_acq_data)
obj_fun.set_acquisition_model(acq_model)
recon = pet.OSMAPOSLReconstructor()
recon.set_objective_function(obj_fun)
recon.set_num_subsets(1)
recon.set_num_subiterations(1)
recon.set_input(simulated_acq_data_w_noise)
if verb:
print('setting up, please wait...')
initial_estimate = image.get_uniform_copy()
recon.set_up(initial_estimate)
if verb:
print('reconstructing...')
recon.set_current_estimate(initial_estimate)
recon.process()
reconstructed_im = recon.get_output()
if not reconstructed_im:
raise AssertionError()
# Reset original verbose-ness
pet.set_verbosity(original_verb)
return 0, 1
#%% NAC reconstruction
tprint('Start NAC Recon')
for i, sino, random in zip(range(len(path_sino)),
sorted_alphanumeric(list_sino),
sorted_alphanumeric(list_rando)):
sino_pet = Pet.AcquisitionData(path_sino + sino)
print(sino)
randoms_pet = Pet.AcquisitionData(path_rando + random)
print(random)
# reconstruct the data (without mu-map)
obj_fun = Pet.make_Poisson_loglikelihood(sino_pet)
acq_model.set_background_term(randoms_pet)
recon.set_objective_function(obj_fun)
initial_image = sino_pet.create_uniform_image(1.0)
image = initial_image
recon.set_up(image)
recon.set_current_estimate(image)
recon.process()
# save recon images
recon_image = recon.get_output()
recon_image.write(path_NAC + 'NAC_' + str(i))
# save Image as .nii
recon_image = Reg.NiftiImageData(recon.get_output())
for i in range(len(time_intervals)-1):
print('Begin reconstruction: Frame {}'.format(i))
# listmode-to-sinogram
lm2sino.set_time_interval(time_intervals[i], time_intervals[i+1])
lm2sino.set_up()
lm2sino.process()
acq_data = lm2sino.get_output()
acq_data.write(working_folder + '/sino/sino'+str(i))
# randoms estimate
randoms = lm2sino.estimate_randoms()
randoms.write(working_folder + '/rando/rando'+str(i))
# reconstruct the data (includes all)
obj_fun = Pet.make_Poisson_loglikelihood(acq_data)
asm_attn.set_up(acq_data)
attn_factors = Pet.AcquisitionData(acq_data)
attn_factors.fill(1.0)
asm_attn.unnormalise(attn_factors)
asm_attn = Pet.AcquisitionSensitivityModel(attn_factors)
asm = Pet.AcquisitionSensitivityModel(asm_norm, asm_attn)
acq_model.set_acquisition_sensitivity(asm)
acq_model.set_background_term(randoms)
obj_fun.set_acquisition_model(acq_model)
recon.set_objective_function(obj_fun)
initial_image = acq_data.create_uniform_image(1.0)
image = initial_image
recon.set_up(image)
recon.set_current_estimate(image)
