def test_scr_reordering_adluru_true_prior_100_iter(self):
from mr_utils.recon.reordering import scr_reordering_adluru
# Use the true image as the prior
prior = np.fft.ifft2(self.kspace)
# Radial undersampling
reduced_kspace = self.kspace * self.radial_mask
# Run the recon
im_est = scr_reordering_adluru(reduced_kspace,
self.radial_mask,
prior=prior,
alpha0=self.alpha0,
alpha1=self.alpha1,
beta2=self.beta2,
niters=100)
# plt.subplot(1,3,1)
# plt.imshow(np.abs(im_est),cmap='gray')
# plt.title('Mine')
# plt.subplot(1,3,2)
# plt.imshow(np.abs(self.recon),cmap='gray')
# plt.title('True')
# plt.subplot(1,3,3)
# plt.imshow(np.abs(im_est - self.recon),cmap='gray')
# plt.title('Diff')
# plt.show()
# Is this too large of a difference? I think it's numerical error...
self.assertTrue(np.allclose(self.recon_100, im_est, rtol=.3))
def test_scr_reordering_adluru_true_prior_50_iter(self):
from mr_utils.recon.reordering import scr_reordering_adluru
# Use the true image as the prior
prior = np.fft.ifft2(self.kspace)
# Radial undersampling
reduced_kspace = self.kspace * self.radial_mask
# Run the recon
im_est = scr_reordering_adluru(reduced_kspace,
self.radial_mask,
prior=prior,
alpha0=self.alpha0,
alpha1=self.alpha1,
beta2=self.beta2,
niters=50)
self.assertTrue(np.allclose(self.recon_50, im_est))
from mr_utils.sim.traj import cartesian_pe
if __name__ == '__main__':
N = 100
reorder = True
m = binary_smiley(N)
k = np.sum(np.abs(np.diff(m)) > 0)
np.random.seed(0)
samp = cartesian_pe(m.shape, undersample=.2, reflines=5)
# Take samples
y = np.fft.fftshift(np.fft.fft2(m)) * samp
# Do convex recon
m_hat = scr_reordering_adluru(y,
samp,
prior=m,
alpha0=1,
alpha1=.001,
beta2=np.finfo(float).eps,
reorder=reorder,
reorder_every_iter=False,
enforce_consistency=True,
niters=1000)
# Look at result
plt.imshow(np.abs(m_hat), cmap='gray')
plt.title('GD Recon, MSE: %e' % compare_mse(np.abs(m_hat), m))
plt.show()