def _get_model(config):
if ('siddon' in config):
model = CompleteLoRsModel('map_model')
listmodedata = ListModeDataWithoutTOF
# kernel_width = None
elif ('siddon_sino' in config):
model = CompleteLoRsModel('map_model')
listmodedata = ListModeDataWithoutTOF
# kernel_width = None
else:
kernel_width = config['tor']['kernel_width']
geo_sigma2_flag = config['tor']['geometry_sigma2_correction']
model = SplitLoRsModel(kernel_width, geo_sigma2_flag, 'map_model')
listmodedata = ListModeDataSplitWithoutTOF
return model, listmodedata
def main(config):
model = CompleteLoRsModel('model', **config['projection_model'])
master_loader = MasterLoader(config['shape'], config['center'],
config['size'])
worker_loader = siddonProjectionLoader(RESOURCE_ROOT + 'ring_nogap.h5',
config['center'], config['size'])
bproj_step = BprojStep('worker/backprojection',
BackProjectionOrdinary(model))
g = LocalBackprojectionGraph(
'backprojection',
MasterGraph('master', loader=master_loader, nb_workers=1),
WorkerGraph('worker', bproj_step, loader=worker_loader, task_index=0))
g.make()
with Session() as sess:
g.run(sess)
def compute(lors, grid, center, size):
# physics_model = SplitLorsModel('map_model', kernel_width=kernel_width)
physics_model = CompleteLoRsModel('map_model')
backprojection_model = BackProjectionOrdinary(physical_model=physics_model)
# map_step = MapStep('compute/effmap', backprojection=backprojection_model)
# t = RingEfficiencyMap('effmap', compute_graph=map_step,
# lors=lors, grid=grid,
# center=center, size=size)
t = RingEfficiencyMap('effmap', backprojection_model, lors, grid, center, size)
with Session() as sess:
t.make()
result = t.run()
# print(result[result>0])
# sess.reset()
return result
def test_uniform_cylinder(self):
config = self.load_config()
model = CompleteLoRsModel(
'model', **config['algorithm']['projection_model']['siddon'])
projection = ProjectionOrdinary(model)
lors = np.load(config['input']['listmode']['path_file'])
projection_data = ListModeData(lors,
np.ones([lors.shape[0]], np.float32))
image_config = config['algorithm']['correction']['atten_correction']
image = np.load(image_config['path_file'])
center = image_config['center']
size = image_config['size']
g = Attenuation('attenuation', projection, projection_data, image,
center, size)
g.make()
with Session() as sess:
value = g.run(sess)
sess.reset()
len_lors = np.power(lors[:, 3] - lors[:, 0], 2) + np.power(
lors[:, 4] - lors[:, 1], 2) + np.power(lors[:, 5] - lors[:, 2], 2)
weight = 1.0 / (np.exp(-value * len_lors) + sys.float_info.min)
previous_result = np.load(
'/mnt/gluster/Techpi/attenuation/test2/result.npy')[:, 7]
assert all_close(previous_result, weight)
def main(config):
model = CompleteLoRsModel('model', **config['projection_model'])
master_loader = MasterLoader(config['shape'], config['center'],
config['size'])
worker_loader = CompleteWorkerLoader(
RESOURCE_ROOT + 'mct_lors_debug.npy',
RESOURCE_ROOT + 'summap.npy',
# "./summap.npy",
config['center'],
config['size'])
recon_step = ReconStep('worker/recon', ProjectionOrdinary(model),
BackProjectionOrdinary(model), mlem_update_normal)
g = LocalReconstructionGraph('reconstruction',
MasterGraph('master',
loader=master_loader,
nb_workers=1),
WorkerGraph('worker',
recon_step,
loader=worker_loader,
task_index=0),
nb_iteration=10)
g.make()
with Session() as sess:
g.run(sess)
def _make_recon_task(self,
task_index,
task_config,
distribution_config=None):
""" Create a specific task object.
A specific task object is built according to the user configuration on task.
Args:
scanner: the scanner used in this task.
job: role of this host (master/worker).
task_index: the index of this task.
task_config: the configuation file of this task.
distribution_config: hosts used to run this task.
Returns:
A task object.
"""
al_config = task_config['algorithm']['projection_model']
im_config = task_config['output']['image']
pj_config = task_config['scanner']['petscanner']
tof_bin, tof_sigma2 = self._get_tof_info()
correction = make_correction(task_config['algorithm']['correction'])
if ('siddon' in al_config):
model = CompleteLoRsModel('model',
tof_bin=tof_bin,
tof_sigma2=tof_sigma2)
worker_loader = CompleteWorkerLoader(
task_config['input']['listmode']['path_file'],
task_config['output']['image']['map_file']['path_file'],
self._scanner, im_config, correction)
elif ('siddon_sino' in al_config):
model = CompleteSinoModel('model', pj_config)
worker_loader = siddonSinogramLoader(
pj_config, task_config['input']['listmode']['path_file'],
"./summap.npy", im_config)
else:
model = SplitLoRsModel(
al_config['tor']['kernel_width'],
al_config['tor']['geometry_sigma2_correction'],
tof_bin=tof_bin,
tof_sigma2=tof_sigma2)
worker_loader = SplitWorkerLoader(
task_config['input']['listmode']['path_file'],
task_config['output']['image']['map_file']['path_file'],
self._scanner, im_config, correction)
master_loader = MasterLoader(self._scanner, im_config)
# decide if use PSF correction
if correction.psf is not None:
recon_step = PSFReconStep('worker/recon',
ProjectionOrdinary(model),
BackProjectionOrdinary(model),
mlem_update)
else:
recon_step = ReconStep('worker/recon', ProjectionOrdinary(model),
BackProjectionOrdinary(model), mlem_update)
if ('mlem' in task_config['algorithm']['recon']):
nb_iteration = task_config['algorithm']['recon']['mlem'][
'nb_iterations']
else:
nb_iteration = task_config['algorithm']['recon']['osem'][
'nb_iterations']
output_filename = task_config['output']['image']['path_dataset_prefix']
g = LocalReconstructionGraph('reconstruction',
MasterGraph('master',
loader=master_loader,
nb_workers=1),
WorkerGraph('worker',
recon_step,
loader=worker_loader,
task_index=task_index),
output_filename=output_filename,
nb_iteration=nb_iteration)
g.make()
with Session() as sess:
g.run(sess)