def test_reconstruction_fista_fft2(self):
""" Test all the registered transformations.
"""
print('Process test FFT2 FISTA')
for image in self.images:
fourier = FFT2(samples=convert_mask_to_locations(
fftshift(self.mask)),
shape=image.shape)
data = fourier.op(image.data)
fourier_op = FFT2(convert_mask_to_locations(fftshift(self.mask)),
shape=image.shape)
print("Process test with image '{0}'...".format(
image.metadata["path"]))
for nb_scale in self.nb_scales:
print("- Number of scales: {0}".format(nb_scale))
for name in self.names:
print(" Transform: {0}".format(name))
linear_op = Wavelet2(wavelet_name=name, nb_scale=4)
gradient_op = GradSynthesis2(data=data,
fourier_op=fourier_op,
linear_op=linear_op)
x_final, transform = sparse_rec_fista(
gradient_op=gradient_op,
linear_op=linear_op,
mu=0,
lambda_init=1.0,
max_nb_of_iter=self.nb_iter,
atol=1e-4,
verbose=0,
get_cost=False)
mismatch = (1. - numpy.mean(
numpy.isclose(x_final, fourier.adj_op(data),
rtol=1e-3)))
print(" mismatch = ", mismatch)
def test_reconstruction_condat_vu_fft2(self):
""" Test all the registered transformations.
"""
print('Process test FFT2 Condat Vu algorithm')
for image in self.images:
fourier = FFT2(samples=convert_mask_to_locations(
fftshift(self.mask)),
shape=image.shape)
data = fourier.op(image.data)
fourier_op = FFT2(samples=convert_mask_to_locations(
fftshift(self.mask)),
shape=image.shape)
print("Process test with image '{0}'...".format(
image.metadata["path"]))
for nb_scale in self.nb_scales:
print("- Number of scales: {0}".format(nb_scale))
for name in self.names:
print(" Transform: {0}".format(name))
linear_op = Wavelet2(wavelet_name=name, nb_scale=4)
gradient_op = GradAnalysis2(data=data,
fourier_op=fourier_op)
x_final, transform = sparse_rec_condatvu(
gradient_op=gradient_op,
linear_op=linear_op,
std_est=0.0,
std_est_method="dual",
std_thr=0,
mu=0,
tau=None,
sigma=None,
relaxation_factor=1.0,
nb_of_reweights=0,
max_nb_of_iter=self.nb_iter,
add_positivity=False,
atol=1e-4,
verbose=0)
mismatch = (1. - numpy.mean(
numpy.isclose(x_final, fourier.adj_op(data),
rtol=1e-3)))
print(" mismatch = ", mismatch)
return
def test_sampling_converters(self):
"""Test the adjoint operator for the 2D non-Cartesian Fourier transform
"""
for i in range(self.max_iter):
print("Process test convert mask to samples test '{0}'...", i)
Nx = numpy.random.randint(8, 512)
Ny = numpy.random.randint(8, 512)
mask = numpy.random.randint(2, size=(Nx, Ny))
samples = convert_mask_to_locations(mask)
recovered_mask = convert_locations_to_mask(samples, (Nx, Ny))
mismatch = (1. - numpy.mean(numpy.allclose(mask, recovered_mask)))
print(" mismatch = ", mismatch)
print(" Test convert mask to samples and it's adjoint passes")
def test_FFT2(self):
"""Test the adjoint operator for the 2D non-Cartesian Fourier transform
"""
for i in range(self.max_iter):
_mask = numpy.random.randint(2, size=(self.N, self.N))
_samples = convert_mask_to_locations(_mask)
print("Process FFT2 test '{0}'...", i)
fourier_op_dir = FFT2(samples=_samples, shape=(self.N, self.N))
fourier_op_adj = FFT2(samples=_samples, shape=(self.N, self.N))
Img = numpy.random.randn(self.N, self.N)
f = numpy.random.randn(self.N, self.N)
f_p = fourier_op_dir.op(Img) / (self.N**2)
I_p = fourier_op_adj.adj_op(f)
x_d = numpy.dot(Img.flatten(), numpy.conj(I_p).flatten())
x_ad = numpy.dot(f_p.flatten(), numpy.conj(f).flatten())
mismatch = (1. - numpy.mean(numpy.isclose(x_d, x_ad, rtol=1e-3)))
print(" mismatch = ", mismatch)
print(" FFT2 adjoint test passes")
from pysap.plugins.mri.reconstruct_3D.fourier import NUFFT, NFFT3, FFT3
from pysap.plugins.mri.reconstruct.fourier import NFFT2, FFT2
import numpy as np
from pysap.plugins.mri.reconstruct_3D.utils import convert_mask_to_locations_3D
from pysap.plugins.mri.reconstruct.utils import convert_mask_to_locations
from modopt.math.metrics import mse
import matplotlib.pyplot as plt
from pysap.plugins.mri.parallel_mri.extract_sensitivity_maps import gridding_nd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
# Sampling pattern (here, random)
_mask = np.ones(np.random.randint(2, size=(512, 512)).shape)
_samples = convert_mask_to_locations(_mask)
_samples_shift = convert_mask_to_locations(np.fft.fftshift(_mask))
# Load images
images = np.load('/volatile/bsarthou/datas/NUFFT/mri_img_2D.npy')
print(images.shape)
# In[3]:
fourier_op_dir_nufft = NUFFT(samples=_samples,
platform='cpu',
shape=(512, 512),
Kd=512,
Jd=3)
fourier_op_dir_nfft = NFFT2(samples=_samples, shape=(512, 512))
fourier_op_dir_fft = FFT2(samples=_samples_shift, shape=(512, 512))
# Generate the kspace
# -------------------
#
# From the 2D brain slice and the acquistion mask, we generate the acquisition
# measurments, the observed kspace.
# We then reconstruct the zero order solution.
# Generate the subsampled kspace
Sl = prod_over_maps(Smaps, SOS)
kspace_data = function_over_maps(pfft.fft2, Sl)
mask = pysap.Image(data=mask)
kspace_data = prod_over_maps(kspace_data, mask.data)
mask.show()
# Get the locations of the kspace samples
kspace_loc = convert_mask_to_locations(mask.data)
#############################################################################
# FISTA optimization
# ------------------
#
# We now want to refine the zero order solution using a FISTA optimization.
# Here no cost function is set, and the optimization will reach the
# maximum number of iterations. Fill free to play with this parameter.
# Start the FISTA reconstruction
max_iter = 1
linear_op = Wavelet2(wavelet_name="UndecimatedBiOrthogonalTransform",
nb_scale=4)
prox_op = Threshold(None)
mask.show() ############################################################################# # Generate the kspace # ------------------- # # From the 2D brain slice and the acquistion mask, we generate the acquisition # measurments, the observed kspace. # We then reconstruct the zero order solution. # Generate the subsampled kspace kspace_mask = pfft.ifftshift(mask.data) kspace_data = pfft.fft2(image.data) * kspace_mask # Get the locations of the kspace samples kspace_loc = convert_mask_to_locations(kspace_mask) # Zero order solution image_rec0 = pysap.Image(data=pfft.ifft2(kspace_data), metadata=image.metadata) image_rec0.show() ############################################################################# # FISTA optimization # ------------------ # # We now want to refine the zero order solution using a FISTA optimization. # Here no cost function is set, and the optimization will reach the # maximum number of iterations. Fill free to play with this parameter. # Start the FISTA reconstruction max_iter = 20