# Load images
[H1, H2] = load_pyobject(folder + H_filename)
[S1, S2] = load_pyobject(folder + S_filename)
[D1, D2] = load_pyobject(folder + D_filename)
# Rescale
H1.rescale(), H2.rescale()
S1.rescale(), S2.rescale()
D1.rescale(), D2.rescale()
# Maximum intensity of the image and standard deviation
# of the noise
if r == 0:
I_max_r0 = max([
np.abs(ktoi(D1.k)[..., 0, 0]).max(),
np.abs(ktoi(D1.k)[..., 1, 0]).max()
])
for n, nlevel in enumerate(noise_levels):
# Standard deviation of the noise
std = nlevel * I_max_r0
# Generate noise in the image domain
noise_1 = np.random.normal(0, std, D1.k.shape) \
+ 1j*np.random.normal(0, std, D1.k.shape)
noise_2 = np.random.normal(0, std, D2.k.shape) \
+ 1j*np.random.normal(0, std, D2.k.shape)
# Noise in the fourier domain
[I1, mask] = load_pyobject(folder + filename)
I2 = -1
else:
[I1, I2, mask] = load_pyobject(folder + filename)
# Export matlab object
I = {'I1': I1, 'I2': I2, 'M': mask}
savemat('inputs/3D_experiments/' + fname + '.mat', {'I': I})
elif folder is not 'inputs/masks/':
# Load kspaces and rescale
if if_exact(fname):
K1 = load_pyobject(folder + filename)
K1.rescale()
I = ktoi(K1.k)
else:
[K1, K2] = load_pyobject(folder + filename)
K1.rescale()
K2.rescale()
I = ktoi(K1.k - K2.k)
# Scale image
I = scale_image(I, mag=False, real=True, compl=True)
# Export matlab object
savemat('inputs/noise_free_images/' + fname + '.mat', {'I': I})
else:
# Load mask
# Load images
[H1, H2] = load_pyobject(folder + H_filename)
[S1, S2] = load_pyobject(folder + S_filename)
[D1, D2] = load_pyobject(folder + D_filename)
# Rescale
H1.rescale(), H2.rescale()
S1.rescale(), S2.rescale()
D1.rescale(), D2.rescale()
# Maximum intensity of the image and standard deviation
# of the noise
if r == 0:
I_max_r0 = max([
np.abs(ktoi(D1.k)[..., 0, 0]).max(),
np.abs(ktoi(D1.k)[..., 1, 0]).max()
])
for n, nlevel in enumerate(noise_levels):
# Standard deviation of the noise
std = nlevel * I_max_r0
# Generate noise in the image domain
noise_1 = np.random.normal(0, std, D1.k.shape) \
+ 1j*np.random.normal(0, std, D1.k.shape)
noise_2 = np.random.normal(0, std, D2.k.shape) \
+ 1j*np.random.normal(0, std, D2.k.shape)
# Noise in the fourier domain
elif trajectory == 'spiral':
traj = Spiral(FOV=FOV,
res=res,
oversampling=2,
lines_per_shot=2,
interleaves=10)
# traj.plot_trajectory()
# Non-cartesian kspace
K = TrajToImage(traj.points, 1000.0 * traj.times, Mxy, r, 50.0)
# Non-uniform fft
x0 = traj.points[0].flatten().reshape((-1, 1))
x1 = traj.points[1].flatten().reshape((-1, 1))
x0 *= res[0] // 2 / x0.max()
x1 *= res[1] // 2 / x1.max()
dcf = (x0**2 + x1**2)**0.5 # density compensation function
kxky = np.concatenate((x0, x1), axis=1) # flattened kspace coordinates
y = K.flatten().reshape((-1, 1)) # flattened kspace measures
image = nufft_adjoint(y * dcf, kxky, res) # inverse nufft
# Show results
fig, axs = plt.subplots(2, 3, figsize=(10, 10))
axs[0, 0].imshow(np.abs(K_c[::2, :]))
axs[0, 1].imshow(np.abs(ktoi(K_c[::2, :])))
axs[0, 2].imshow(np.angle(ktoi(K_c[::2, :])))
axs[1, 0].imshow(np.abs(itok(image)))
axs[1, 1].imshow(np.abs(image))
axs[1, 2].imshow(np.angle(image))
plt.show()
def to_img(self):
return ktoi(self.k)