def setUp(self):
self.image1 = pet.ImageData(
os.path.join(examples_data_path('PET'), 'thorax_single_slice',
'emission.hv'))
self.image2 = pet.ImageData(
os.path.join(examples_data_path('PET'), 'thorax_single_slice',
'emission.hv'))
def setUp(self):
image1 = reg.ImageData(os.path.join(
examples_data_path('Registration'),'test2.nii.gz')
)
image2 = reg.ImageData(os.path.join(
examples_data_path('Registration'),'test2.nii.gz')
)
self.image1 = image1
self.image2 = image2
def setUp(self):
image1 = mr.AcquisitionData(
os.path.join(examples_data_path('MR'),
'simulated_MR_2D_cartesian.h5'))
image2 = mr.AcquisitionData(
os.path.join(examples_data_path('MR'),
'simulated_MR_2D_cartesian.h5'))
image1.sort()
image2.sort()
self.image1 = image1
self.image2 = image2
def setUp(self):
if os.path.exists(
os.path.join(examples_data_path('PET'), 'mMR',
'mMR_template_span11_small.hs')):
template = pet.AcquisitionData(
os.path.join(examples_data_path('PET'), 'mMR',
'mMR_template_span11_small.hs'))
self.image1 = template.get_uniform_copy(0)
self.image2 = template.get_uniform_copy(0)
# assert False
self.set_storage_scheme()
def setUp(self):
path = os.path.join(examples_data_path('PET'), 'thorax_single_slice',
'template_sinogram.hs')
if os.path.exists(path):
template = pet.AcquisitionData(path)
self.image1 = template.get_uniform_copy(0)
self.image2 = template.get_uniform_copy(0)
# assert False
self.set_storage_scheme()
def setUp(self):
if has_sirf:
os.chdir(examples_data_path('PET'))
# Copy files to a working folder and change directory to where these files are.
# We do this to avoid cluttering your SIRF files. This way, you can delete
# working_folder and start from scratch.
shutil.rmtree('working_folder/brain', True)
shutil.copytree('brain', 'working_folder/brain')
os.chdir('working_folder/brain')
self.cwd = os.getcwd()
def setUp(self):
acq_data = mr.AcquisitionData(
os.path.join(examples_data_path('MR'),
'simulated_MR_2D_cartesian.h5'))
# ad1.sort()
preprocessed_data = mr.preprocess_acquisition_data(acq_data)
recon = mr.FullySampledReconstructor()
recon.set_input(preprocessed_data)
recon.process()
image_data = recon.get_output()
self.image1 = image_data
self.image2 = image_data * 1
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
__version__ = '0.1.0'
from docopt import docopt
args = docopt(__doc__, version=__version__)
import time
# import SIRF utilities
from sirf.Utilities import examples_data_path, existing_filepath, error
# import MR engine types
from sirf.Gadgetron import AcquisitionData, Reconstructor
# process command-line options
data_file = args['--file']
data_path = args['--path']
if data_path is None:
data_path = examples_data_path('MR')
output_file = args['--output']
def main():
# locate the input data
input_file = existing_filepath(data_path, data_file)
acq_data = AcquisitionData(input_file)
# create reconstruction object
# Rather than using a predefined image reconstruction object, here a new
# image reconstruction object is created by concatinating multiple gadgets
# (for more information on Gadgetron and its gadgets please see:
# https://github.com/gadgetron/.).
# Parameters for individual gadgets can be defined either during the
# creation of the reconstruction object:
# import engine module
import sirf.Reg
from sirf.Utilities import examples_data_path
exec('import p' + args['--eng_ref'] + ' as eng_ref')
exec('import p' + args['--eng_flo'] + ' as eng_flo')
# process command-line options
ref_file = args['--ref']
flo_file = args['--flo']
algo = args['--algo']
pad = args['--pad']
# if using the default for any, need to get the examples folder
if (ref_file or flo_file) is None:
examples_path = examples_data_path('Registration')
# reference
if ref_file is None:
ref_file = examples_path + "/test.nii.gz"
# floating
if flo_file is None:
flo_file = examples_path + "/test2.nii.gz"
# parse the transformations
trans_filenames_str = args['--trans_filenames']
trans_types_str = args['--trans_types']
trans_filenames = list()
trans_types = list()
if trans_filenames_str:
def test_main(rec=False, verb=False, throw=True):
data_file = 'simulated_MR_2D_cartesian.h5'
data_path = examples_data_path('MR')
output_file = None
type_to_save = 'all'
show_plot = False
algorithm = 'SimpleReconGadget'
# locate the input data
input_file = existing_filepath(data_path, data_file)
acq_data = AcquisitionData(input_file)
if algorithm == 'SimpleReconGadget':
extra_gadgets = [algorithm]
else:
extra_gadgets = [algorithm, 'GenericReconFieldOfViewAdjustmentGadget']
# create reconstruction object
# Rather than using a predefined image reconstruction object, here a new
# image reconstruction object is created by concatinating multiple gadgets
# (for more information on Gadgetron and its gadgets please see:
# https://github.com/gadgetron/.).
# Parameters for individual gadgets can be defined either during the
# creation of the reconstruction object:
# e.g. AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)
# or by giving a gadget a label (cf. label ex: for the last gadget)
# and using set_gadget_property(label, propery, value).
# The gadgets will be concatenated and will be executed as soon as
# process() is called.
recon_gadgets = ['NoiseAdjustGadget',
'AsymmetricEchoAdjustROGadget',
'RemoveROOversamplingGadget',
'AcquisitionAccumulateTriggerGadget(trigger_dimension=repetition)',
'BucketToBufferGadget(split_slices=true, verbose=false)'] \
+ extra_gadgets + \
['ImageArraySplitGadget',
'ex:ExtractGadget'
]
recon = Reconstructor(recon_gadgets)
# ExtractGadget defines which type of image should be returned:
# none 0
# magnitude 1
# real 2
# imag 4
# phase 8
# in this example '5' returns both magnitude and imaginary part
## recon.set_gadget_property('ex', 'extract_mask', 5)
# === THE ABOVE IS OBSOLETE, NOW SHOULD USE ===>
if type_to_save=='mag' or type_to_save=='all':
recon.set_gadget_property('ex', 'extract_magnitude', True)
if type_to_save=='imag' or type_to_save=='all':
recon.set_gadget_property('ex', 'extract_imag', True)
# provide raw k-space data as input
recon.set_input(acq_data)
# optionally set Gadgetron server host and port
recon.set_host('localhost')
# On VM you can try a port other than the default 9002, e.g. 9003, by taking
# the following steps:
# 1) in ~/devel/install/share/gadgetron/config/gadgetron.xml replace
# <port>9002</port> with <port>9003</port>
# 2) go to Settings->Network->Advanced->Port Forwarding and add new rule
# (click on green + in the upper right corner) with Host and Guest ports
# set to 9003
# 3) uncomment the next line
#recon.set_port('9003')
# Note: each gadget chain can run on a different VM - to try, start two VMs
# and do the above steps 1 and 2 on one of them, then add
# recon.set_port('9003') before recon.process in grappa_detail.py
# (where preprocessing will still run on default port 9002).
# perform reconstruction
recon.process()
# retrieve reconstructed image data
image_data = recon.get_output()
# show reconstructed image data
if show_plot:
for im in range(image_data.number()):
image = image_data.image(im)
# image types series
# magnitude 1 0
# phase 2 3000
# real 3 1000
# imag 4 2000
im_type = image.image_type()
im_series = image.image_series_index()
print('image: %d, type: %d, series: %d' % (im, im_type, im_series))
image_data.show(title = 'Images magnitude and imaginary part')
if output_file is not None:
filename = output_file
i = filename.find('.')
if i < 0:
ext = 'h5'
else:
ext = filename[i + 1:]
filename = filename[:i]
print('writing to %s' % (filename + '.' + ext))
image_data.write(filename, ext=ext)
return 0, 1
args = docopt(__doc__, version=__version__)
def check_file_exists(filename):
"""Check file exists, else throw error."""
if not path.isfile(filename):
raise error('File not found: %s' % filename)
# process command-line options
data_path = args['--path']
if data_path is None:
# default to data/examples/PET/mMR
# Note: seem to need / even on Windows
#data_path = os.path.join(examples_data_path('PET'), 'mMR')
data_path = examples_data_path('PET') + '/mMR'
print('Finding files in %s' % data_path)
# Sinogram. if sino not found, get the one in the example data
sino_file = existing_filepath(data_path, args['--sino'])
# Attenuation - image
attn_im_file = existing_filepath(data_path, args['--attn'])
# Norm - ECAT8
norm_e8_file = existing_filepath(data_path, args['--norm'])
# Attn transformation
trans = args['--trans']
if trans:
check_file_exists(trans)
import numpy as np
from random import randint
from sirf.Utilities import error
from docopt import docopt
__version__ = '0.1.0'
args = docopt(__doc__, version=__version__)
# path
data_path = args['--path']
# PET sino
if args['--PET']:
template_PET_raw_path = args['--PET']
else:
template_PET_raw_path = os.path.join(examples_data_path('PET'),
'mMR/mMR_template_single_slice.hs')
# MR sino
if args['--MR']:
template_MR_raw_path = args['--MR']
else:
template_MR_raw_path = os.path.join(examples_data_path('MR'),
'grappa2_1rep.h5')
# Num motion states
num_ms = int(args['--num_ms'])
def get_resampler_from_tm(tm, image):
"""returns a NiftyResampler object for the specified transform matrix and image"""
def tearDown(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)
