def _run_reco(args):
# Read input data
kspace_data, trajectory, noise, data_par, coils = read_data(
pathtofile=args.pathtofile,
undersampling_factor=args.undersampling_factor)
# Setup parameters
parameter = setup_parameter_dict(args.configfile,
rawdata=kspace_data,
trajectory=trajectory,
data_par=data_par)
# Decorrelate Coil channels if noise scan is present
kspace_data, coils = _decor_noise(data=kspace_data,
noise=noise,
par=parameter["Data"],
coils=coils)
# Get coil sensitivities in the parameter dict
estimate_coil_sensitivities(kspace_data,
trajectory,
parameter,
coils=coils)
# Save coils if not present
_save_coil_(pathtofile=args.pathtofile,
undersampling_factor=args.undersampling_factor,
par=parameter["Data"])
# Get operator
MRImagingOperator = linop.MRIImagingModel(parameter, trajectory)
cgs = solver.CGReco(data_par=parameter["Data"],
optimizer_par=parameter["Optimizer"])
print("Starting reconstruction...")
cgs.set_operator(MRImagingOperator)
# Start reconstruction
# Data needs to be multiplied with the sqrt of dense_cor to assure that
# forward and adjoint application of the NUFFT is adjoint with each other.
# dens_cor itself is saved in the par dict as the sqrt.
recon_result, residuals = cgs.optimize(data=kspace_data *
parameter["FFT"]["dens_cor"])
# Single Coil images after FFT
single_coil_images = cgs.operator.NUFFT.adjoint(
kspace_data * parameter["FFT"]["dens_cor"])
# Store results
save_to_file(recon_result, residuals, single_coil_images,
parameter["Data"], args)
def setUp(self):
parser = tmpArgs()
parser.streamed = False
parser.devices = [0]
parser.use_GPU = True
par = {}
setupPar(par)
self.op = linop.MRIImagingModel(par, par["traj"])
self.opinfwd = (np.random.randn(par["Data"]["image_dim"],
par["Data"]["image_dim"]) +
1j * np.random.randn(par["Data"]["image_dim"],
par["Data"]["image_dim"]))
self.opinadj = (
np.random.randn(par["Data"]["num_coils"], par["Data"]["num_proj"],
par["Data"]["num_reads"]) + 1j *
np.random.randn(par["Data"]["num_coils"], par["Data"]["num_proj"],
par["Data"]["num_reads"]))
self.opinfwd = self.opinfwd.astype(DTYPE)
self.opinadj = self.opinadj.astype(DTYPE)