def get_resamplers(static_image, dynamic_array, dvf_array, output_path):
resamplers = []
static_image_path = "{0}/temp_static.nii".format(output_path)
dynamic_array_path = "{0}/temp_dynamic.nii".format(output_path)
dvf_array_path = "{0}/temp_dvf.nii".format(output_path)
for j in range(len(dynamic_array)):
resampler = reg.NiftyResample()
static_image.write(static_image_path)
dynamic_array[j].write(dynamic_array_path)
dvf_array[j].write(dvf_array_path)
temp_static = reg.NiftiImageData(static_image_path)
temp_dynamic = reg.NiftiImageData(dynamic_array_path)
temp_dvf = reg.NiftiImageData3DDeformation(dvf_array_path)
resampler.set_reference_image(temp_static)
resampler.set_floating_image(temp_dynamic)
resampler.add_transformation(temp_dvf)
resampler.set_interpolation_type_to_linear()
resamplers.append(resampler)
return resamplers
def objective_function(optimise_array, static_image, dynamic_path, dvf_path,
weighted_normalise, dynamic_data_magnitude):
static_image.fill(
np.reshape(optimise_array,
static_image.as_array().astype(np.double).shape))
objective_value = 0.0
for i in range(len(dynamic_path)):
dynamic_image = reg.NiftiImageData(dynamic_path[i])
dvf_image = reg.NiftiImageData3DDeformation(dvf_path[i])
resampler = reg.NiftyResample()
resampler.set_reference_image(static_image)
resampler.set_floating_image(dynamic_image)
resampler.add_transformation(dvf_image)
resampler.set_interpolation_type_to_cubic_spline()
objective_value = objective_value + (np.nansum(
np.square(dynamic_image.as_array().astype(np.double) -
((np.nansum(dynamic_image.as_array().astype(np.double),
dtype=np.double) / dynamic_data_magnitude) *
warp_image_forward(resampler, static_image)),
dtype=np.double),
dtype=np.double) * weighted_normalise[i])
print("Objective function value: {0}".format(str(objective_value)))
return objective_value
def get_dynamic_data_magnitude(dynamic_path):
dynamic_data_magnitude = 0.0
for i in range(len(dynamic_path)):
dynamic_data_magnitude = dynamic_data_magnitude + np.nansum(
reg.NiftiImageData(dynamic_path[i]).as_array().astype(np.double),
dtype=np.double)
return dynamic_data_magnitude
def op_test(static_image, output_path):
static_image_path = "{0}/temp_static.nii".format(output_path)
static_image.write(static_image_path)
temp_static = reg.NiftiImageData(static_image_path)
temp_at = reg.AffineTransformation()
temp_at_array = temp_at.as_array()
temp_at_array[0][0] = 1.25
temp_at_array[1][1] = 1.25
temp_at_array[2][2] = 1.25
temp_at_array[3][3] = 1.25
temp_at = reg.AffineTransformation(temp_at_array)
resampler = reg.NiftyResample()
resampler.set_reference_image(temp_static)
resampler.set_floating_image(temp_static)
resampler.add_transformation(temp_at)
resampler.set_interpolation_type_to_linear()
warp = warp_image_forward(resampler, temp_static)
warped_image = static_image.clone()
warped_image.fill(warp)
warped_image.write("{0}/op_test_warp_forward.nii".format(output_path))
difference = temp_static.as_array().astype(np.double) - warp
difference_image = temp_static.clone()
difference_image.fill(difference)
difference_image.write(
"{0}/op_test_warp_forward_difference.nii".format(output_path))
warp = warp_image_adjoint(resampler, temp_static)
warped_image = temp_static.clone()
warped_image.fill(warp)
warped_image.write("{0}/op_test_warp_adjoint.nii".format(output_path))
difference = temp_static.as_array().astype(np.double) - warp
difference_image = temp_static.clone()
difference_image.fill(difference)
difference_image.write(
"{0}/warp_adjoint_difference.nii".format(output_path))
return True
def gradient_function(optimise_array, resampler, dynamic_images, static_image,
output_path):
static_image.fill(
np.reshape(optimise_array,
static_image.as_array().astype(np.double).shape))
gradient_value = static_image.clone()
gradient_value.fill(0.0)
adjoint_image = static_image.clone()
for i in range(len(dynamic_images)):
static_image.write("{0}/temp_static.nii".format(output_path))
dynamic_images[i].write("{0}/temp_dynamic.nii".format(output_path))
temp_static = reg.NiftiImageData(
"{0}/temp_static.nii".format(output_path))
temp_dynamic = reg.NiftiImageData(
"{0}/temp_dynamic.nii".format(output_path))
adjoint_image.fill(
warp_image_forward(resampler[i], temp_static) -
temp_dynamic.as_array().astype(np.double))
gradient_value.fill((gradient_value.as_array().astype(np.double) +
warp_image_adjoint(resampler[i], adjoint_image)))
gradient_value.write("{0}/gradient.nii".format(output_path))
print(
"Max gradient value: {0}, Min gradient value: {1}, Mean gradient value: {2}, Gradient norm: {3}"
.format(
str(gradient_value.as_array().astype(np.double).max()),
str(gradient_value.as_array().astype(np.double).min()),
str(
np.nanmean(gradient_value.as_array().astype(np.double),
dtype=np.double)),
str(np.linalg.norm(gradient_value.as_array().astype(np.double)))))
return np.ravel(gradient_value.as_array().astype(np.double)).astype(
np.double)
def output_input(static_image, dynamic_path, dvf_path, output_path):
static_image.write("{0}/static_image.nii".format(output_path))
for i in range(len(dynamic_path)):
dynamic_image = reg.NiftiImageData(dynamic_path[i])
dvf_image = reg.NiftiImageData3DDeformation(dvf_path[i])
dynamic_image.write("{0}/dynamic_image_{1}.nii".format(
output_path, str(i)))
dvf_image.write("{0}/dvf_image_{1}.nii".format(output_path, str(i)))
return True
def test_for_adj(static_image, dvf_array, output_path):
static_image_path = "{0}/temp_static.nii".format(output_path)
dvf_array_path = "{0}/temp_dvf.nii".format(output_path)
for i in range(len(dvf_array)):
static_image.write(static_image_path)
dvf_array[i].write(dvf_array_path)
temp_static = reg.NiftiImageData(static_image_path)
temp_dvf = reg.NiftiImageData3DDeformation(dvf_array_path)
resampler = reg.NiftyResample()
resampler.set_reference_image(temp_static)
resampler.set_floating_image(temp_static)
resampler.add_transformation(temp_dvf)
resampler.set_interpolation_type_to_linear()
warp = warp_image_forward(resampler, temp_static)
warped_image = static_image.clone()
warped_image.fill(warp)
warped_image.write("{0}/warp_forward_{1}.nii".format(
output_path, str(i)))
difference = temp_static.as_array().astype(np.double) - warp
difference_image = temp_static.clone()
difference_image.fill(difference)
difference_image.write("{0}/warp_forward_difference_{1}.nii".format(
output_path, str(i)))
warp = warp_image_adjoint(resampler, temp_static)
warped_image = temp_static.clone()
warped_image.fill(warp)
warped_image.write("{0}/warp_adjoint_{1}.nii".format(
output_path, str(i)))
difference = temp_static.as_array().astype(np.double) - warp
difference_image = temp_static.clone()
difference_image.fill(difference)
difference_image.write("{0}/warp_adjoint_difference_{1}.nii".format(
output_path, str(i)))
return True
def gradient_function(optimise_array, static_image, dynamic_path, dvf_path,
weighted_normalise, dynamic_data_magnitude):
static_image.fill(
np.reshape(optimise_array,
static_image.as_array().astype(np.double).shape))
gradient_value = static_image.clone()
gradient_value.fill(0.0)
adjoint_image = static_image.clone()
for i in range(len(dynamic_path)):
dynamic_image = reg.NiftiImageData(dynamic_path[i])
dvf_image = reg.NiftiImageData3DDeformation(dvf_path[i])
resampler = reg.NiftyResample()
resampler.set_reference_image(static_image)
resampler.set_floating_image(dynamic_image)
resampler.add_transformation(dvf_image)
resampler.set_interpolation_type_to_cubic_spline()
adjoint_image.fill((
(np.nansum(dynamic_image.as_array().astype(np.double),
dtype=np.double) / dynamic_data_magnitude) *
warp_image_forward(resampler, static_image)) -
dynamic_image.as_array().astype(np.double))
gradient_value.fill((gradient_value.as_array().astype(np.double) +
(warp_image_adjoint(resampler, adjoint_image) *
weighted_normalise[i])))
# gradient_value.write("{0}/gradient.nii".format(output_path))
print(
"Max gradient value: {0}, Mean gradient value: {1}, Gradient norm: {2}"
.format(
str(np.amax(gradient_value.as_array().astype(np.double))),
str(
np.nanmean(
np.abs(gradient_value.as_array().astype(np.double),
dtype=np.double))),
str(np.linalg.norm(gradient_value.as_array().astype(np.double)))))
return np.ravel(gradient_value.as_array().astype(np.double)).astype(
np.double)
def back_warp(static_path, dvf_path, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path, mode=0o770)
for i in range(len(dvf_path)):
static_image = reg.NiftiImageData(static_path)
dvf_image = reg.NiftiImageData3DDeformation(dvf_path[i])
resampler = reg.NiftyResample()
resampler.set_reference_image(static_image)
resampler.set_floating_image(static_image)
resampler.add_transformation(dvf_image)
resampler.set_interpolation_type_to_cubic_spline()
warped_static_image = warp_image_forward(resampler, static_image)
static_image.fill(warped_static_image)
static_image.write("{0}/back_warped_{1}.nii".format(
output_path, str(i)))
return True
def optimise(input_data_path, data_split, weighted_normalise_path,
input_dvf_path, dvf_split, output_path, do_op_test, do_reg,
do_test_for_adj, do_blind_start, do_opt, do_back_warp, prefix):
if not os.path.exists(output_path):
os.makedirs(output_path, mode=0o770)
new_dvf_path = "{0}/new_dvfs/".format(output_path)
if not os.path.exists(new_dvf_path):
os.makedirs(new_dvf_path, mode=0o770)
# get static and dynamic paths
dynamic_path = get_data_path(input_data_path, data_split)
dynamic_data_magnitude = get_dynamic_data_magnitude(dynamic_path)
static_path = "{0}/static_image.nii".format(output_path)
# load static object for dvf registration
static_image = reg.NiftiImageData(dynamic_path[0])
static_image.write(static_path)
if do_op_test:
op_test(static_image, output_path)
dvf_path = None
if do_test_for_adj or do_opt or do_back_warp:
# if do reg the calc dvf if not load
if do_reg:
dvf_path = register_data(static_path, dynamic_path, output_path)
else:
dvf_path = get_dvf_path(input_dvf_path, dvf_split)
# fix dvf header and load dvf objects
dvf_path = edit_header(dvf_path, new_dvf_path)
# sum the dynamic data into the static data
for i in range(1, len(dynamic_path)):
static_image.fill(
static_image.as_array().astype(np.double) +
reg.NiftiImageData(dynamic_path[i]).as_array().astype(np.double))
static_image.write(static_path)
# test for adj
if do_test_for_adj:
test_for_adj(static_image, dvf_path, output_path)
output_input(static_image, dynamic_path, dvf_path, output_path)
# initial static image
initial_static_image = static_image.clone()
if do_blind_start:
initial_static_image.fill(1.0)
initial_static_image.write("{0}/initial_static_image_{1}.nii".format(
output_path, prefix))
# array to optimise
optimise_array = initial_static_image.as_array().astype(np.double)
# array bounds
bounds = []
for j in range(len(np.ravel(optimise_array))):
bounds.append((0.01, 10.0))
tol = 0.000000000009
if do_opt:
weighted_normalise = parser.parser(weighted_normalise_path,
"weighted_normalise:=")
if weighted_normalise is None:
weighted_normalise = parser.parser(weighted_normalise_path,
"normalise_array:=")
for i in range(len(weighted_normalise)):
weighted_normalise[i] = float(weighted_normalise[i])
# optimise
optimise_array = np.reshape(
scipy.optimize.minimize(objective_function,
np.ravel(optimise_array),
args=(static_image, dynamic_path, dvf_path,
weighted_normalise,
dynamic_data_magnitude),
method="L-BFGS-B",
jac=gradient_function,
bounds=bounds,
tol=tol,
options={
"disp": True
}).x, optimise_array.shape)
# output
static_image.fill(optimise_array)
static_image.write("{0}/optimiser_output_{1}.nii".format(
output_path, prefix))
difference = static_image.as_array().astype(
np.double) - initial_static_image.as_array().astype(np.double)
difference_image = initial_static_image.clone()
difference_image.fill(difference)
static_image.write("{0}/optimiser_output_difference_{1}.nii".format(
output_path, prefix))
if do_back_warp:
back_warp(static_path, dvf_path, "{0}/back_warp/".format(output_path))
multiple = 1.0
nan_optimise_array = optimise_array
nan_optimise_array[nan_optimise_array < 0.01] = np.nan
nan_optimise_array = nan_optimise_array - np.nanmin(nan_optimise_array)
# array bounds
bounds = [(0.01, 10.0)]
# optimise
multiple = scipy.optimize.minimize(suv_objective_function,
np.asarray(multiple),
args=(nan_optimise_array),
method="L-BFGS-B",
tol=tol,
bounds=bounds,
options={
"disp": True
}).x[0]
# output
nan_optimise_array = nan_optimise_array - np.nanmin(nan_optimise_array)
nan_optimise_array = np.nan_to_num(nan_optimise_array)
nan_optimise_array[nan_optimise_array < 0.01] = 0.0
nan_optimise_array = nan_optimise_array * multiple
static_image.fill(nan_optimise_array)
static_image.write("{0}/suv_optimiser_output_{1}.nii".format(
output_path, prefix))
naive_suv_optimise_array = optimise_array / 0.25
static_image.fill(naive_suv_optimise_array)
static_image.write("{0}/naive_suv_optimiser_output_{1}.nii".format(
output_path, prefix))
def main():
# Make output folder if necessary
if not os.path.isdir(data_path):
os.makedirs(data_path)
os.chdir(data_path)
# Download the data
print("downloading brainweb data...")
[FDG_arr, uMap_arr, T1_arr] = download_data()
# Get template PET image from template raw
template_PET_raw = pet.AcquisitionData(template_PET_raw_path)
template_PET_im = pet.ImageData(template_PET_raw)
# Get template MR image from template raw
template_MR_raw = mr.AcquisitionData(template_MR_raw_path)
template_MR_raw.sort_by_time()
template_MR_raw = mr.preprocess_acquisition_data(template_MR_raw)
template_MR_im = simple_mr_recon(template_MR_raw)
# Number voxels in (x,y) directions - nxy (dictated by MR image)
nxy = template_MR_im.get_geometrical_info().get_size()[0]
if nxy != template_MR_im.get_geometrical_info().get_size()[1]:
raise AssertionError("Expected square image in (x,y) direction")
if template_MR_im.get_geometrical_info().get_size()[2] > 1:
raise AssertionError("Only currently designed for 2D image")
# Create PET image
dim = (1, nxy, nxy)
size = FDG_arr.shape
z_slice = size[0] // 2
xy_min = (size[1] - nxy) // 2
xy_max = xy_min + nxy
voxel_size = template_PET_im.voxel_sizes()
template_PET_im.initialise(dim, voxel_size)
# Reorient template MR image with template PET image such that it's compatible with both
template_MR_im.reorient(template_PET_im.get_geometrical_info())
############################################################################################
# Crop brainweb image to right size
############################################################################################
# Convert brainweb's (127,344,344) to desired size
print("Cropping brainweb images to size...")
[FDG, uMap, T1] = [crop_brainweb(template_MR_im, im_arr, z_slice, xy_min, xy_max) \
for im_arr in [FDG_arr, uMap_arr, T1_arr]]
############################################################################################
# Apply motion
############################################################################################
print("Resampling images to different motion states...")
FDGs = [0] * num_ms
uMaps = [0] * num_ms
T1s = [0] * num_ms
for ind in range(num_ms):
# Get TM for given motion state
tm = get_and_save_tm(ind)
# Get resampler
res = get_resampler_from_tm(tm, template_MR_im)
# Resample
for im, modality in zip([FDG, uMap, T1], ['FDG', 'uMap', 'T1']):
resampled = res.forward(im)
if modality == 'FDG':
FDGs[ind] = resampled
elif modality == 'uMap':
uMaps[ind] = resampled
elif modality == 'T1':
T1s[ind] = resampled
else:
raise AssertionError("Unknown modality")
reg.NiftiImageData(resampled).write(modality + '_ms' + str(ind))
############################################################################################
# MR: create k-space data for motion states
############################################################################################
# Create coil sensitivity data
print("Calculating coil sensitivity map...")
csm = mr.CoilSensitivityData()
csm.smoothness = 500
csm.calculate(template_MR_raw)
# Create interleaved sampling
print("Creating raw k-space data for MR motion states...")
mvec = []
for ind in range(num_ms):
mvec.append(np.arange(ind, template_MR_raw.number(), num_ms))
# Go through motion states and create k-space
for ind in range(num_ms):
acq_ms = template_MR_raw.new_acquisition_data(empty=True)
# Set first two (??) acquisition
acq_ms.append_acquisition(template_MR_raw.acquisition(0))
acq_ms.append_acquisition(template_MR_raw.acquisition(1))
# Add motion resolved data
for jnd in range(len(mvec[ind])):
if mvec[ind][jnd] < template_MR_raw.number() - 1 and mvec[ind][
jnd] > 1: # Ensure first and last are not added twice
cacq = template_MR_raw.acquisition(mvec[ind][jnd])
acq_ms.append_acquisition(cacq)
# Set last acquisition
acq_ms.append_acquisition(
template_MR_raw.acquisition(template_MR_raw.number() - 1))
# Create acquisition model
AcqMod = mr.AcquisitionModel(acq_ms, T1s[ind])
AcqMod.set_coil_sensitivity_maps(csm)
# Forward project!
acq_ms_sim = AcqMod.forward(T1s[ind])
# Save
print("writing: " + 'raw_T1_ms' + str(ind) + '.h5')
acq_ms_sim.write('raw_T1_ms' + str(ind) + '.h5')
############################################################################################
# PET: create sinograms
############################################################################################
print("Creating singorams for PET motion states...")
stir_uMap = template_PET_im.clone()
stir_FDG = template_PET_im.clone()
for ind in range(num_ms):
stir_uMap.fill(uMaps[ind].as_array())
stir_FDG.fill(FDGs[ind].as_array())
am = get_acquisition_model(stir_uMap, template_PET_raw)
FDG_sino = am.forward(stir_FDG)
FDG_sino = add_noise(0.25, FDG_sino)
FDG_sino.write('raw_FDG_ms' + str(ind))
def main():
# file paths to data
input_data_path = parser.parser(sys.argv[1], "data_path:=")
data_split = parser.parser(sys.argv[1], "data_split:=")
input_dvf_path = parser.parser(sys.argv[1], "dvf_path:=")
dvf_split = parser.parser(sys.argv[1], "dvf_split:=")
output_path = parser.parser(sys.argv[1], "output_path:=")
do_op_test = parser.parser(sys.argv[1], "do_op_test:=")
do_reg = parser.parser(sys.argv[1], "do_reg:=")
do_test_for_adj = parser.parser(sys.argv[1], "do_test_for_adj:=")
for i in range(len(input_data_path)):
if not os.path.exists(output_path[i]):
os.makedirs(output_path[i], mode=0o770)
new_dvf_path = "{0}/new_dvfs/".format(output_path[i])
if not os.path.exists(new_dvf_path):
os.makedirs(new_dvf_path, mode=0o770)
# get static and dynamic paths
dynamic_path = get_data_path(input_data_path[i], data_split[i])
# load dynamic objects
dynamic_array = []
for j in range(len(dynamic_path)):
dynamic_array.append(reg.NiftiImageData(dynamic_path[j]))
static_path = "{0}/static_path.nii".format(output_path[i])
# load static objects
static_image = reg.NiftiImageData(dynamic_path[0])
for j in range(1, len(dynamic_path)):
static_image.fill(static_image.as_array().astype(np.double) +
dynamic_array[j].as_array().astype(np.double))
static_image.write(static_path)
if bool(distutils.util.strtobool(do_op_test[i])):
op_test(static_image, output_path[i])
# if do reg the calc dvf if not load
if bool(distutils.util.strtobool(do_reg[i])):
dvf_path = register_data(static_path, dynamic_path, output_path[i])
else:
dvf_path = get_dvf_path(input_dvf_path[i], dvf_split[i])
# fix dvf header and load dvf objects
dvf_path = edit_header(dvf_path, new_dvf_path)
dvf_array = []
for j in range(len(dvf_path)):
dvf_array.append(reg.NiftiImageData3DDeformation(dvf_path[j]))
# create object to get forward and adj
resamplers = get_resamplers(static_image, dynamic_array, dvf_array,
output_path[i])
# test for adj
if bool(distutils.util.strtobool(do_test_for_adj[i])):
test_for_adj(static_image, dvf_array, output_path[i])
output_input(static_image, dynamic_array, dvf_array,
output_path[i])
# initial static image
initial_static_image = static_image.clone()
# array to optimise
optimise_array = static_image.as_array().astype(np.double)
# array bounds
bounds = []
for j in range(len(np.ravel(optimise_array.copy()))):
bounds.append((-np.inf, np.inf))
# optimise
optimise_array = np.reshape(
scipy.optimize.minimize(objective_function,
np.ravel(optimise_array).astype(np.double),
args=(resamplers, dynamic_array,
static_image, output_path[i]),
method="L-BFGS-B",
jac=gradient_function,
bounds=bounds,
tol=0.0000000001,
options={
"disp": True
}).x, optimise_array.shape)
# output
static_image.fill(optimise_array)
static_image.write("{0}/optimiser_output_{1}.nii".format(
output_path[i], str(i)))
difference = static_image.as_array().astype(
np.double) - initial_static_image.as_array().astype(np.double)
difference_image = initial_static_image.clone()
difference_image.fill(difference)
static_image.write("{0}/optimiser_output_difference_{1}.nii".format(
output_path[i], str(i)))
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, nxny)
image = initial_image
recon.set_up(image)
recon.set_current_estimate(image)
recon.process()
# save recon images
recon_image = Reg.NiftiImageData(recon.get_output())
recon_image.write(path_NAC + 'NAC_' + str(i))
image = recon.get_output()
# apply gaussian filter with 3mm fwhm
gaussian_filter = Pet.SeparableGaussianImageFilter()
gaussian_filter.set_fwhms((3, 3, 3))
#gaussian_filter.set_max_kernel_sizes((10, 10, 2))
gaussian_filter.set_normalise()
gaussian_filter.set_up(image)
gaussian_filter.apply(image)
# save Image as .nii
smoothed_image = Reg.NiftiImageData(image)
smoothed_image.write(path_smooth + 'smooth_' + str(i))
def callback_save(iteration, objective_value, solution):
"""Callback function to save images"""
if (iteration + 1) % save_interval == 0:
out = solution if not nifti else reg.NiftiImageData(solution)
out.write(outp_file + "_iters" + str(iteration + 1))
print('Begin resampling: {}'.format(image))
resampler_im.clear_transformations()
resampler_im.set_floating_image(flo)
resampler_im.add_transformation(tm)
new_im = resampler_im.forward(flo)
new_im.write(working_folder + '/moco/moco_'+str(num))
print('Resampling successful: {}'.format(image))
tprint('Finish Resampling')
#%% define RTA method
# define initial image (first image, first frame)
initial = Reg.NiftiImageData(working_folder + '/moco/moco_0.nii')
initial_array = initial.as_array()
# sum over all images (as array)
for image in sorted_alphanumeric(os.listdir(path_moco))[1:]:
print(image)
array = Reg.NiftiImageData(path_moco + image).as_array()
initial_array += array
# create image
final_image = Reg.NiftiImageData(working_folder + '/moco/moco_0.nii')
final_image.fill(initial_array)
final_image.write('final_image_RTA.nii')
tprint('DONE!')
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)
recon.process()
# save recon images
recon_image = recon.get_output()
recon_image.write(working_folder + '/recon/recon' + str(i))
# save Image as .nii
recon_image = Reg.NiftiImageData(recon.get_output())
recon_image.write(working_folder + '/floates/recon'+str(i))
print('Reconstruction successful: Frame {}'.format(i))
tprint('Finish Recon')
#%% create folder for motion corrected images
path_moco = working_folder + '/moco/'
if not os.path.exists(path_moco):
os.makedirs(path_moco, mode=0o770)
print('Create Folder: {}'.format(path_moco))
#%% convert a array to a SIRF transformation matrix and then resample the float image
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())
recon_image.write(path_NAC + 'NAC_' + str(i))
print('Reconstruction successful: Frame {}'.format(i))
tprint('Finish NAC Recon')
# reconstruct the data (without mu-map)
obj_fun = Pet.make_Poisson_loglikelihood(acq_data)
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)
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())
recon_image.write(path_NAC + '/nii/NAC_' + str(i))
print('Reconstruction successful: Frame {}'.format(i))
tprint('Finish Recon NAC')
#%% SPM registration NAC
# define reference image (first image) and float-path, NAC
ref_file = path_NAC + '/nii/' + 'NAC_0.nii'
ref = Eng_ref.ImageData(ref_file)
flo_path = path_NAC + '/nii/'
tprint('Start Reg for NAC')