def calculate(self, data, method=None):
'''
Calculates coil sensitivity maps from coil images or sorted
acquisitions.
data : either AcquisitionData or CoilImages
method: either SRSS (Square Root of the Sum of Squares, default) or
Inati
'''
if isinstance(data, AcquisitionData):
if data.is_sorted() is False:
print('WARNING: acquisitions may be in a wrong order')
if self.handle is not None:
pyiutil.deleteDataHandle(self.handle)
self.handle = pygadgetron.cGT_CoilSensitivities('')
check_status(self.handle)
if method is not None:
method_name, parm_list = name_and_parameters(method)
parm = parse_arglist(parm_list)
else:
method_name = 'SRSS'
parm = {}
if isinstance(data, AcquisitionData):
assert data.handle is not None
_set_int_par\
(self.handle, 'coil_sensitivity', 'smoothness', self.smoothness)
try_calling(pygadgetron.cGT_computeCoilSensitivities\
(self.handle, data.handle))
elif isinstance(data, CoilImageData):
assert data.handle is not None
if method_name == 'Inati':
# if not HAVE_ISMRMRDTOOLS:
try:
from ismrmrdtools import coils
except:
raise error('Inati method requires ismrmrd-python-tools')
nz = data.number()
for z in range(nz):
ci = numpy.squeeze(data.as_array(z))
(csm, rho) = coils.calculate_csm_inati_iter(ci)
self.append(csm.astype(numpy.complex64))
elif method_name == 'SRSS':
if 'niter' in parm:
nit = int(parm['niter'])
_set_int_par\
(self.handle, 'coil_sensitivity', 'smoothness', nit)
try_calling(pygadgetron.cGT_computeCSMsFromCIs\
(self.handle, data.handle))
else:
raise error('Unknown method %s' % method_name)
else:
raise error('Cannot calculate coil sensitivities from %s' % \
repr(type(data)))
def process(self):
'''
Processes the input with the gadget chain.
'''
if self.input_data is None:
raise error('no input data')
try_calling(pygadgetron.cGT_reconstructImages\
(self.handle, self.input_data.handle))
def as_array(self, ci_num):
'''
Returns specified coil images array as Numpy ndarray.
ci_num: coil images array (slice) number
'''
nc, nz, ny, nx = self.image_dimensions()
if nx == 0 or ny == 0 or nz == 0 or nc == 0:
raise error('image data not available')
re = numpy.ndarray((nc, nz, ny, nx), dtype=numpy.float32)
im = numpy.ndarray((nc, nz, ny, nx), dtype=numpy.float32)
pygadgetron.cGT_getCoilData\
(self.handle, ci_num, re.ctypes.data, im.ctypes.data)
return re + 1j * im
def process(self, input_data=None):
'''
Returns the output from the chain for specified input.
input_data: AcquisitionData
'''
if input_data is not None:
self.set_input(input_data)
if self.input_data is None:
raise error('input data not set')
assert_validity(self.input_data, AcquisitionData)
acquisitions = AcquisitionData()
acquisitions.handle = pygadgetron.cGT_processAcquisitions\
(self.handle, self.input_data.handle)
check_status(acquisitions.handle)
self.output_data = acquisitions
return acquisitions
def process(self, input_data=None):
'''
Returns the output from the chain.
input_data: ImageData
'''
if input_data is not None:
self.set_input(input_data)
if self.input_data is None:
raise error('input data not set')
assert_validity(self.input_data, ImageData)
image = ImageData()
image.handle = pygadgetron.cGT_processImages\
(self.handle, self.input_data.handle)
check_status(image.handle)
self.output_data = image
return image
def show(self, slice = None, title = None, cmap = 'gray', power = 0.2, \
postpone = False):
'''Displays xy-cross-section(s) of images.'''
assert self.handle is not None
if not HAVE_PYLAB:
print('pylab not found')
return
data = numpy.transpose(self.as_array(), (1, 0, 2))
nz = data.shape[0]
if type(slice) == type(1):
if slice < 0 or slice >= nz:
return
ns = 1
slice = [slice]
## show_2D_array('slice %d' % slice, data[slice,:,:])
## return
elif slice is None:
ns = nz
slice = range(nz)
else:
try:
ns = len(slice)
except:
raise error('wrong slice list')
if title is None:
title = 'Selected images'
if ns >= 16:
tiles = (4, 4)
else:
tiles = None
f = 0
while f < ns:
t = min(f + 16, ns)
err = show_3D_array(abs(data), index = slice[f : t], \
tile_shape = tiles, \
label = 'coil', xlabel = 'samples', \
ylabel = 'readouts', \
suptitle = title, cmap = cmap, power = power, \
show = (t == ns) and not postpone)
f = t
'\n# --------------------------------------------------------------------------------- #\n'
)
sys.stderr.write(
'# Finished complex resampling test.\n')
sys.stderr.write(
'# --------------------------------------------------------------------------------- #\n'
)
time.sleep(0.5)
def test():
raw_mr_filename = SIRF_PATH + "/data/examples/MR/grappa2_1rep.h5"
if os.path.isfile(SIRF_PATH +
"/data/examples/MR/zenodo/dicom_as_nifti.nii"):
nifti_filename = SIRF_PATH + "/data/examples/MR/zenodo/dicom_as_nifti.nii"
mr_recon_h5_filename = SIRF_PATH + "/data/examples/MR/zenodo/SIRF_recon.h5"
else:
nifti_filename = SIRF_PATH + "/data/examples/Registration/test2.nii.gz"
try_stirtonifti(nifti_filename)
if mr_recon_h5_filename:
try_gadgetrontonifti(nifti_filename, mr_recon_h5_filename)
try_complex_resample(raw_mr_filename)
if __name__ == "__main__":
try:
test()
except:
raise error("Error encountered.")
def check_file_exists(filename):
"""Check file exists, else throw error."""
if not path.isfile(filename):
raise error('File not found: %s' % filename)
def main():
###########################################################################
# Parse input files
###########################################################################
if trans_pattern is None:
raise AssertionError("--trans missing")
if sino_pattern is None:
raise AssertionError("--sino missing")
trans_files = sorted(glob(trans_pattern))
sino_files = sorted(glob(sino_pattern))
attn_files = sorted(glob(attn_pattern))
rand_files = sorted(glob(rand_pattern))
num_ms = len(sino_files)
# Check some sinograms found
if num_ms == 0:
raise AssertionError("No sinograms found!")
# Should have as many trans as sinos
if num_ms != len(trans_files):
raise AssertionError("#trans should match #sinos. "
"#sinos = " + str(num_ms) + ", #trans = " +
str(len(trans_files)))
# If any rand, check num == num_ms
if len(rand_files) > 0 and len(rand_files) != num_ms:
raise AssertionError("#rand should match #sinos. "
"#sinos = " + str(num_ms) + ", #rand = " +
str(len(rand_files)))
# For attn, there should be 0, 1 or num_ms images
if len(attn_files) > 1 and len(attn_files) != num_ms:
raise AssertionError("#attn should be 0, 1 or #sinos")
###########################################################################
# Read input
###########################################################################
if trans_type == "tm":
trans = [reg.AffineTransformation(file) for file in trans_files]
elif trans_type == "disp":
trans = [
reg.NiftiImageData3DDisplacement(file) for file in trans_files
]
elif trans_type == "def":
trans = [reg.NiftiImageData3DDeformation(file) for file in trans_files]
else:
raise error("Unknown transformation type")
sinos_raw = [pet.AcquisitionData(file) for file in sino_files]
attns = [pet.ImageData(file) for file in attn_files]
rands = [pet.AcquisitionData(file) for file in rand_files]
# Loop over all sinograms
sinos = [0] * num_ms
for ind in range(num_ms):
# If any sinograms contain negative values
# (shouldn't be the case), set them to 0
sino_arr = sinos_raw[ind].as_array()
if (sino_arr < 0).any():
print("Input sinogram " + str(ind) +
" contains -ve elements. Setting to 0...")
sinos[ind] = sinos_raw[ind].clone()
sino_arr[sino_arr < 0] = 0
sinos[ind].fill(sino_arr)
else:
sinos[ind] = sinos_raw[ind]
# If rebinning is desired
segs_to_combine = 1
if args['--numSegsToCombine']:
segs_to_combine = int(args['--numSegsToCombine'])
views_to_combine = 1
if args['--numViewsToCombine']:
views_to_combine = int(args['--numViewsToCombine'])
if segs_to_combine * views_to_combine > 1:
sinos[ind] = sinos[ind].rebin(segs_to_combine, views_to_combine)
# only print first time
if ind == 0:
print(f"Rebinned sino dimensions: {sinos[ind].dimensions()}")
###########################################################################
# Initialise recon image
###########################################################################
if initial_estimate:
image = pet.ImageData(initial_estimate)
else:
# Create image based on ProjData
image = sinos[0].create_uniform_image(0.0, (nxny, nxny))
# If using GPU, need to make sure that image is right size.
if use_gpu:
dim = (127, 320, 320)
spacing = (2.03125, 2.08626, 2.08626)
# elif non-default spacing desired
elif args['--dxdy']:
dim = image.dimensions()
dxdy = float(args['--dxdy'])
spacing = (image.voxel_sizes()[0], dxdy, dxdy)
if use_gpu or args['--dxdy']:
image.initialise(dim=dim, vsize=spacing)
image.fill(0.0)
###########################################################################
# Set up resamplers
###########################################################################
resamplers = [get_resampler(image, trans=tran) for tran in trans]
###########################################################################
# Resample attenuation images (if necessary)
###########################################################################
resampled_attns = None
if len(attns) > 0:
resampled_attns = [0] * num_ms
# if using GPU, dimensions of attn and recon images have to match
ref = image if use_gpu else None
for i in range(len(attns)):
# if we only have 1 attn image, then we need to resample into
# space of each gate. However, if we have num_ms attn images, then
# assume they are already in the correct position, so use None as
# transformation.
tran = trans[i] if len(attns) == 1 else None
# If only 1 attn image, then resample that. If we have num_ms attn
# images, then use each attn image of each frame.
attn = attns[0] if len(attns) == 1 else attns[i]
resam = get_resampler(attn, ref=ref, trans=tran)
resampled_attns[i] = resam.forward(attn)
###########################################################################
# Set up acquisition models
###########################################################################
print("Setting up acquisition models...")
if not use_gpu:
acq_models = num_ms * [pet.AcquisitionModelUsingRayTracingMatrix()]
else:
acq_models = num_ms * [pet.AcquisitionModelUsingNiftyPET()]
for acq_model in acq_models:
acq_model.set_use_truncation(True)
acq_model.set_cuda_verbosity(verbosity)
# If present, create ASM from ECAT8 normalisation data
asm_norm = None
if norm_file:
asm_norm = pet.AcquisitionSensitivityModel(norm_file)
# Loop over each motion state
for ind in range(num_ms):
# Create attn ASM if necessary
asm_attn = None
if resampled_attns:
asm_attn = get_asm_attn(sinos[ind], resampled_attns[i],
acq_models[ind])
# Get ASM dependent on attn and/or norm
asm = None
if asm_norm and asm_attn:
if ind == 0:
print("ASM contains norm and attenuation...")
asm = pet.AcquisitionSensitivityModel(asm_norm, asm_attn)
elif asm_norm:
if ind == 0:
print("ASM contains norm...")
asm = asm_norm
elif asm_attn:
if ind == 0:
print("ASM contains attenuation...")
asm = asm_attn
if asm:
acq_models[ind].set_acquisition_sensitivity(asm)
if len(rands) > 0:
acq_models[ind].set_background_term(rands[ind])
# Set up
acq_models[ind].set_up(sinos[ind], image)
###########################################################################
# Set up reconstructor
###########################################################################
print("Setting up reconstructor...")
# Create composition operators containing acquisition models and resamplers
C = [
CompositionOperator(am, res, preallocate=True)
for am, res in zip(*(acq_models, resamplers))
]
# Configure the PDHG algorithm
if args['--normK'] and not args['--onlyNormK']:
normK = float(args['--normK'])
else:
kl = [KullbackLeibler(b=sino, eta=(sino * 0 + 1e-5)) for sino in sinos]
f = BlockFunction(*kl)
K = BlockOperator(*C)
# Calculate normK
print("Calculating norm of the block operator...")
normK = K.norm(iterations=10)
print("Norm of the BlockOperator ", normK)
if args['--onlyNormK']:
exit(0)
# Optionally rescale sinograms and BlockOperator using normK
scale_factor = 1. / normK if args['--normaliseDataAndBlock'] else 1.0
kl = [
KullbackLeibler(b=sino * scale_factor, eta=(sino * 0 + 1e-5))
for sino in sinos
]
f = BlockFunction(*kl)
K = BlockOperator(*C) * scale_factor
# If preconditioned
if precond:
def get_nonzero_recip(data):
"""Get the reciprocal of a datacontainer. Voxels where input == 0
will have their reciprocal set to 1 (instead of infinity)"""
inv_np = data.as_array()
inv_np[inv_np == 0] = 1
inv_np = 1. / inv_np
data.fill(inv_np)
tau = K.adjoint(K.range_geometry().allocate(1))
get_nonzero_recip(tau)
tmp_sigma = K.direct(K.domain_geometry().allocate(1))
sigma = 0. * tmp_sigma
get_nonzero_recip(sigma[0])
def precond_proximal(self, x, tau, out=None):
"""Modify proximal method to work with preconditioned tau"""
pars = {
'algorithm':
FGP_TV,
'input':
np.asarray(x.as_array() / tau.as_array(), dtype=np.float32),
'regularization_parameter':
self.lambdaReg,
'number_of_iterations':
self.iterationsTV,
'tolerance_constant':
self.tolerance,
'methodTV':
self.methodTV,
'nonneg':
self.nonnegativity,
'printingOut':
self.printing
}
res, info = regularisers.FGP_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'], pars['nonneg'],
self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
out *= tau
return out
FGP_TV.proximal = precond_proximal
print("Will run proximal with preconditioned tau...")
# If not preconditioned
else:
sigma = float(args['--sigma'])
# If we need to calculate default tau
if args['--tau']:
tau = float(args['--tau'])
else:
tau = 1 / (sigma * normK**2)
if regularisation == 'none':
G = IndicatorBox(lower=0)
elif regularisation == 'FGP_TV':
r_iterations = float(args['--reg_iters'])
r_tolerance = 1e-7
r_iso = 0
r_nonneg = 1
r_printing = 0
device = 'gpu' if use_gpu else 'cpu'
G = FGP_TV(r_alpha, r_iterations, r_tolerance, r_iso, r_nonneg,
r_printing, device)
else:
raise error("Unknown regularisation")
if precond:
def PDHG_new_update(self):
"""Modify the PDHG update to allow preconditioning"""
# save previous iteration
self.x_old.fill(self.x)
self.y_old.fill(self.y)
# Gradient ascent for the dual variable
self.operator.direct(self.xbar, out=self.y_tmp)
self.y_tmp *= self.sigma
self.y_tmp += self.y_old
self.f.proximal_conjugate(self.y_tmp, self.sigma, out=self.y)
# Gradient descent for the primal variable
self.operator.adjoint(self.y, out=self.x_tmp)
self.x_tmp *= -1 * self.tau
self.x_tmp += self.x_old
self.g.proximal(self.x_tmp, self.tau, out=self.x)
# Update
self.x.subtract(self.x_old, out=self.xbar)
self.xbar *= self.theta
self.xbar += self.x
PDHG.update = PDHG_new_update
# Get filename
outp_file = outp_prefix
if descriptive_fname:
if len(attn_files) > 0:
outp_file += "_wAC"
if norm_file:
outp_file += "_wNorm"
if use_gpu:
outp_file += "_wGPU"
outp_file += "_Reg-" + regularisation
if regularisation == 'FGP_TV':
outp_file += "-alpha" + str(r_alpha)
outp_file += "-riters" + str(r_iterations)
if args['--normK']:
outp_file += '_userNormK' + str(normK)
else:
outp_file += '_calcNormK' + str(normK)
if args['--normaliseDataAndBlock']:
outp_file += '_wDataScale'
else:
outp_file += '_noDataScale'
if not precond:
outp_file += "_sigma" + str(sigma)
outp_file += "_tau" + str(tau)
else:
outp_file += "_wPrecond"
outp_file += "_nGates" + str(len(sino_files))
if resamplers is None:
outp_file += "_noMotion"
pdhg = PDHG(f=f,
g=G,
operator=K,
sigma=sigma,
tau=tau,
max_iteration=num_iters,
update_objective_interval=update_obj_fn_interval,
x_init=image,
log_file=outp_file + ".log")
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))
pdhg.run(iterations=num_iters,
callback=callback_save,
verbose=True,
very_verbose=True)
if visualisations:
# show reconstructed image
out = pdhg.get_output()
out_arr = out.as_array()
z = out_arr.shape[0] // 2
show_2D_array('Reconstructed image', out.as_array()[z, :, :])
pylab.show()
attn_pattern = str(args['--attn']).replace('%', '*')
rand_pattern = str(args['--rand']).replace('%', '*')
num_iters = int(args['--iter'])
regularisation = args['--reg']
trans_type = args['--trans_type']
if attn_pattern is None:
attn_pattern = ""
if rand_pattern is None:
rand_pattern = ""
# Norm
norm_file = args['--norm']
if norm_file:
if not os.path.isfile(norm_file):
raise error("Norm file not found: " + norm_file)
# Number of voxels
nxny = int(args['--nxny'])
# Output file
outp_prefix = args['--outp']
# Initial estimate
initial_estimate = args['--initial']
visualisations = True if args['--visualisations'] and have_pylab else False
nifti = True if args['--nifti'] else False
use_gpu = True if args['--gpu'] else False
descriptive_fname = True if args['--descriptive_fname'] else False
update_obj_fn_interval = int(args['--update_obj_fn_interval'])
