def try_stirtonifti():
time.sleep(0.5)
sys.stderr.write(
'\n# --------------------------------------------------------------------------------- #\n'
)
sys.stderr.write(
'# Starting STIR to Nifti test...\n')
sys.stderr.write(
'# --------------------------------------------------------------------------------- #\n'
)
time.sleep(0.5)
import pSTIR
# Input filenames
nifti_filename = SIRF_PATH + '/data/examples/Registration/test2.nii.gz'
# Load the image as a NiftiImageData3D
image_nifti = pReg.NiftiImageData3D(nifti_filename)
# Read as STIRImageData, convert to NiftiImageData3D and save to file
image_stir = pSTIR.ImageData(nifti_filename)
image_nifti_from_stir = pReg.NiftiImageData3D(image_stir)
image_nifti_from_stir.write('results/stir_to_nifti.nii',
image_nifti.get_original_datatype())
# Compare the two
if image_nifti != image_nifti_from_stir:
raise AssertionError("Conversion from STIR to Nifti failed.")
time.sleep(0.5)
sys.stderr.write(
'\n# --------------------------------------------------------------------------------- #\n'
)
sys.stderr.write(
'# Finished STIR to Nifti test.\n')
sys.stderr.write(
'# --------------------------------------------------------------------------------- #\n'
)
time.sleep(0.5)
def try_stir_to_sirfreg():
time.sleep(0.5)
sys.stderr.write(
'\n# --------------------------------------------------------------------------------- #\n'
)
sys.stderr.write(
'# Starting STIR to SIRFReg test...\n')
sys.stderr.write(
'# --------------------------------------------------------------------------------- #\n'
)
time.sleep(0.5)
# Open stir image
pet_image_data = pSTIR.ImageData(ref_aladin_filename)
image_data_from_stir = pSIRFReg.NiftiImageData3D(pet_image_data)
# Now fill the stir and sirfreg images with 1 and 100, respectively
pet_image_data.fill(1.)
image_data_from_stir.fill(100)
if pet_image_data.as_array().max() == image_data_from_stir.get_max():
raise AssertionError()
# Fill the stir image with the sirfreg
image_data_from_stir.copy_data_to(pet_image_data)
if pet_image_data.as_array().max() != image_data_from_stir.get_max():
raise AssertionError()
time.sleep(0.5)
sys.stderr.write(
'\n# --------------------------------------------------------------------------------- #\n'
)
sys.stderr.write(
'# Finished STIR to SIRFReg test.\n')
sys.stderr.write(
'# --------------------------------------------------------------------------------- #\n'
)
time.sleep(0.5)
filter = pet.TruncateToCylinderProcessor()
filter.apply(image)
#%% go to directory with input files
# adapt this path to your situation (or start everything in the relevant directory)
os.chdir(pet.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')
#%% We will first create some simulated data from ground-truth images
#%% Read in images
image = pet.ImageData('emission.hv')
image_array = image.as_array() * .05
image.fill(image_array)
mu_map = pet.ImageData('attenuation.hv')
mu_map_array = mu_map.as_array()
#%% bitmap display of images
slice_num = image_array.shape[0] // 2
plt.figure()
#plt.subplot(1,2,1);
imshow(image_array[slice_num, :, :, ], [], 'emission image')
#plt.subplot(1,2,2);
#imshow(mu_map_array[slice,:,:,], [], 'attenuation image');
#%% save max for future displays
cmax = image_array.max() * .6
def main():
# output goes to files
msg_red = pet.MessageRedirector('info.txt', 'warn.txt', 'errr.txt')
# create acquisition model
acq_model = pet.AcquisitionModelUsingRayTracingMatrix()
# PET acquisition data to be read from the file specified by --file option
print('raw data: %s' % raw_data_file)
acq_data = pet.AcquisitionData(raw_data_file)
# Noisy data for testing
noisy_data = acq_data.clone()
noisy_data_array = np.random.poisson(acq_data.as_array() / 10)
noisy_data.fill(noisy_data_array)
# create filter that zeroes the image outside a cylinder of the same
# diameter as the image xy-section size
filter = pet.TruncateToCylinderProcessor()
# create initial image estimate
image_size = (111, 111, 31)
voxel_size = (3, 3, 3.375) # voxel sizes are in mm
image = pet.ImageData()
image.initialise(image_size, voxel_size)
image.fill(1.0)
filter.apply(image)
# create objective function
obj_fun = pet.make_Poisson_loglikelihood(noisy_data)
obj_fun.set_acquisition_model(acq_model)
obj_fun.set_num_subsets(num_subsets)
obj_fun.set_up(image)
# create new obj_fun to get sensitivity image for one subset for dePierro MAPEM
obj_fun2 = pet.make_Poisson_loglikelihood(noisy_data)
obj_fun2.set_acquisition_model(acq_model)
obj_fun2.set_num_subsets(1)
obj_fun2.set_up(image)
sensitivity_image = obj_fun2.get_subset_sensitivity(0)
# create new noise-free obj_fun to use for guidance in dePierro MAPEM
obj_fun3 = pet.make_Poisson_loglikelihood(acq_data)
obj_fun3.set_acquisition_model(acq_model)
obj_fun3.set_num_subsets(num_subsets)
obj_fun3.set_up(image)
# uniform weights
weightsUniform = np.ones([sensitivity_image.as_array().size, 27],
dtype='float')
weightsUniform = weightsUniform / 27.0
# noise free recon with beta = 0 for guidance
image_noiseFree = my_dePierroMap \
(image, obj_fun3, 0, filter, num_subsets, num_subiterations, weightsUniform, sensitivity_image)
# create a Prior for computing Bowsher weights
myPrior = pr.Prior(sensitivity_image.as_array().shape)
weightsBowsher = myPrior.BowshserWeights(image_noiseFree.as_array(), 10)
weightsBowsher = np.float64(weightsBowsher / 10.0)
# dePierro MAPEM with uniform and Bowsher weights
beta = 5000.0
image_dp_b = my_dePierroMap \
(image, obj_fun, beta, filter, num_subsets, num_subiterations, weightsBowsher, sensitivity_image)
image_dp_u = my_dePierroMap \
(image, obj_fun, beta, filter, num_subsets, num_subiterations, weightsUniform, sensitivity_image)
# show reconstructed images at z = 20
image_dp_b_array = image_dp_b.as_array()
image_dp_u_array = image_dp_u.as_array()
show_2D_array('Noise free image', image_noiseFree.as_array()[20, :, :])
show_2D_array('DP Bowsher', image_dp_b_array[20, :, :])
show_2D_array('DP Uniform', image_dp_u_array[20, :, :])
show_2D_array('Difference DP',
image_dp_b_array[20, :, :] - image_dp_u_array[20, :, :])
