def numsim_epi():
##
# Original image: Shep-Logan Phantom
##
N = 128
FOV = 256e-3
ph = resize(shepp_logan_phantom(), (N, N)) #.astype(complex)
plt.imshow(np.abs(ph), cmap='gray')
plt.title('Original phantom')
plt.axis('off')
plt.colorbar()
plt.show()
brain_mask = mask_by_threshold(ph)
# Floodfill from point (0, 0)
ph_holes = ~(flood_fill(brain_mask, (0, 0), 1).astype(int)) + 2
mask = brain_mask + ph_holes
##
# EPI k-space trajectory
##
dt = 4e-6
ktraj = ssEPI_2d(N, FOV) # k-space trajectory
plt.plot(ktraj[:, 0], ktraj[:, 1])
plt.title('EPI trajectory')
plt.show()
Ta = ktraj.shape[0] * dt
T = (np.arange(ktraj.shape[0]) * dt).reshape(ktraj.shape[0], 1)
seq_params = {
'N': N,
'Npoints': ktraj.shape[0],
'Nshots': 1,
't_readout': Ta,
't_vector': T
}
##
# Simulated field map
##
# fmax_v = [50, 75, 100] # Hz
fmax_v = [100, 150, 200]
i = 0
or_corrupted = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_CPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_fsCPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_MFI = np.zeros((N, N, len(fmax_v)), dtype='complex')
for fmax in fmax_v:
# field_map = fieldmap_sim.realistic(np.abs(ph), mask, fmax)
SL_smooth = gaussian(ph, sigma=3)
field_map = cv2.normalize(SL_smooth, None, -fmax, fmax,
cv2.NORM_MINMAX)
field_map = np.round(field_map * mask)
field_map[np.where(field_map == -0.0)] = 0
plt.imshow(field_map, cmap='gray')
plt.title('Field Map +/-' + str(fmax) + ' Hz')
plt.colorbar()
plt.axis('off')
plt.show()
##
# Corrupted images
##
or_corrupted[:, :,
i], EPI_ksp = ORC.add_or_CPR(ph, ktraj, field_map, 'EPI',
seq_params)
corrupt = (np.abs(or_corrupted[:, :, i]) -
np.abs(or_corrupted[..., i]).min()) / (
np.abs(or_corrupted[:, :, i]).max() -
np.abs(or_corrupted[..., i]).min())
# plt.imshow(np.abs(or_corrupted[:,:,i]),cmap='gray')
plt.imshow(corrupt, cmap='gray')
plt.colorbar()
plt.title('Corrupted Image')
plt.axis('off')
plt.show()
###
# Corrected images
###
or_corrected_CPR[:, :,
i] = ORC.correct_from_kdat('CPR', EPI_ksp, ktraj,
field_map, seq_params,
'EPI')
or_corrected_fsCPR[:, :,
i] = ORC.correct_from_kdat('fsCPR', EPI_ksp, ktraj,
field_map, seq_params,
'EPI')
or_corrected_MFI[:, :,
i] = ORC.correct_from_kdat('MFI', EPI_ksp, ktraj,
field_map, seq_params,
'EPI')
# or_corrected_CPR[:, :, i] = ORC.CPR(or_corrupted[:, :, i], 'im', ktraj, field_map, 'EPI', seq_params)
#
# or_corrected_fsCPR[:, :, i] = ORC.fs_CPR(or_corrupted[:, :, i], 'im', ktraj, field_map, 2, 'EPI', seq_params)
#
# or_corrected_MFI[:, :, i] = ORC.MFI(or_corrupted[:, :, i], 'im', ktraj, field_map, 2, 'EPI', seq_params)
i += 1
##
# Plot
##
im_stack = np.stack(
(np.squeeze(or_corrupted), np.squeeze(or_corrected_CPR),
np.squeeze(or_corrected_fsCPR), np.squeeze(or_corrected_MFI)))
# np.save('im_stack.npy',im_stack)
cols = ('Corrupted Image', 'CPR Correction', 'fs-CPR Correction',
'MFI Correction')
row_names = ('-/+ 100 Hz', '-/+ 150 Hz', '-/+ 200 Hz')
plot_correction_results(im_stack, cols, row_names)
def numsim_cartesian():
N = 192
ph = resize(shepp_logan_phantom(), (N, N))
ph = ph.astype(complex)
plt.imshow(np.abs(ph), cmap='gray')
plt.title('Original phantom')
plt.axis('off')
plt.colorbar()
plt.show()
brain_mask = mask_by_threshold(ph)
# Floodfill from point (0, 0)
ph_holes = ~(flood_fill(brain_mask, (0, 0), 1).astype(int)) + 2
mask = brain_mask + ph_holes
##
# Cartesian k-space trajectory
##
dt = 10e-6 # grad raster time
ktraj_cart = np.arange(0, N * dt, dt).reshape(1, N)
ktraj_cart = np.tile(ktraj_cart, (N, 1))
plt.imshow(ktraj_cart, cmap='gray')
plt.title('Cartesian trajectory')
plt.show()
##
# Simulated field map
##
fmax_v = [1600, 3200, 4800] # Hz correspontig to 25, 50 and 75 ppm at 3T
i = 0
or_corrupted = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_CPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_fsCPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_MFI = np.zeros((N, N, len(fmax_v)), dtype='complex')
for fmax in fmax_v:
field_map = fieldmap_sim.realistic(np.abs(ph), mask, fmax)
plt.imshow(field_map, cmap='gray')
plt.title('Field Map')
plt.colorbar()
plt.axis('off')
plt.show()
##
# Corrupted images
##
or_corrupted[:, :, i], _ = ORC.add_or_CPR(ph, ktraj_cart, field_map)
corrupt = (np.abs(or_corrupted[:, :, i]) -
np.abs(or_corrupted[..., i]).min()) / (
np.abs(or_corrupted[:, :, i]).max() -
np.abs(or_corrupted[..., i]).min())
#plt.imshow(np.abs(or_corrupted[:,:,i]),cmap='gray')
plt.imshow(corrupt, cmap='gray')
plt.colorbar()
plt.title('Corrupted Image')
plt.axis('off')
plt.show()
###
# Corrected images
###
or_corrected_CPR[:, :, i] = ORC.CPR(or_corrupted[:, :, i], 'im',
ktraj_cart, field_map)
or_corrected_fsCPR[:, :, i] = ORC.fs_CPR(or_corrupted[:, :, i], 'im',
ktraj_cart, field_map, 2)
or_corrected_MFI[:, :, i] = ORC.MFI(or_corrupted[:, :, i], 'im',
ktraj_cart, field_map, 2)
i += 1
##
# Plot
##
im_stack = np.stack(
(np.squeeze(or_corrupted), np.squeeze(or_corrected_CPR),
np.squeeze(or_corrected_fsCPR), np.squeeze(or_corrected_MFI)))
cols = ('Corrupted Image', 'CPR Correction', 'fs-CPR Correction',
'MFI Correction')
row_names = ('-/+ 1600 Hz', '-/+ 3200 Hz', '-/+ 4800 Hz')
plot_correction_results(im_stack, cols, row_names)
def numsim_spiral():
N = 128 # ph.shape[0]
ph = resize(shepp_logan_phantom(), (N, N)) #.astype(complex)
plt.imshow(np.abs(ph), cmap='gray')
plt.title('Original phantom')
plt.axis('off')
plt.colorbar()
plt.show()
brain_mask = mask_by_threshold(ph)
# Floodfill from point (0, 0)
ph_holes = ~(flood_fill(brain_mask, (0, 0), 1).astype(int)) + 2
mask = brain_mask + ph_holes
##
# Spiral k-space trajectory
##
dt = 4e-6
ktraj = np.load('sample_data/SS_sprial_ktraj.npy') # k-space trajectory
plt.plot(ktraj.real, ktraj.imag)
plt.title('Spiral trajectory')
plt.show()
#ktraj_dcf = np.load('test_data/ktraj_noncart_dcf.npy').flatten() # density compensation factor
t_ro = ktraj.shape[0] * dt
T = (np.arange(ktraj.shape[0]) * dt).reshape(ktraj.shape[0], 1)
seq_params = {
'N': ph.shape[0],
'Npoints': ktraj.shape[0],
'Nshots': ktraj.shape[1],
't_readout': t_ro,
't_vector': T
} #, 'dcf': ktraj_dcf}
##
# Simulated field map
##
fmax_v = [100, 150, 200] # Hz
i = 0
or_corrupted = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_CPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_fsCPR = np.zeros((N, N, len(fmax_v)), dtype='complex')
or_corrected_MFI = np.zeros((N, N, len(fmax_v)), dtype='complex')
for fmax in fmax_v:
SL_smooth = gaussian(ph, sigma=3)
field_map = cv2.normalize(SL_smooth, None, -fmax, fmax,
cv2.NORM_MINMAX)
field_map = np.round(field_map * mask)
field_map[np.where(field_map == -0.0)] = 0
###
plt.imshow(field_map, cmap='gray')
plt.title('Field Map +/-' + str(fmax) + ' Hz')
plt.colorbar()
plt.axis('off')
plt.show()
##
# Corrupted images
##
or_corrupted[:, :, i], ksp_corrupted = ORC.add_or_CPR(
ph, ktraj, field_map, 1, seq_params)
corrupt = (np.abs(or_corrupted[:, :, i]) -
np.abs(or_corrupted[..., i]).min()) / (
np.abs(or_corrupted[:, :, i]).max() -
np.abs(or_corrupted[..., i]).min())
#plt.imshow(np.abs(or_corrupted[:,:,i]),cmap='gray')
plt.imshow(corrupt, cmap='gray')
plt.colorbar()
plt.title('Corrupted Image')
plt.axis('off')
plt.show()
###
# Corrected images
###
or_corrected_CPR[:, :,
i] = ORC.correct_from_kdat('CPR', ksp_corrupted,
ktraj, field_map,
seq_params, 1)
or_corrected_fsCPR[:, :,
i] = ORC.correct_from_kdat('fsCPR', ksp_corrupted,
ktraj, field_map,
seq_params, 1)
or_corrected_MFI[:, :,
i] = ORC.correct_from_kdat('MFI', ksp_corrupted,
ktraj, field_map,
seq_params, 1)
# or_corrected_CPR[:, :, i] = ORC.CPR(or_corrupted[:, :, i], 'im', ktraj, field_map, 1, seq_params)
#
# or_corrected_fsCPR[:, :, i] = ORC.fs_CPR(or_corrupted[:, :, i], 'im', ktraj, field_map, 2, 1, seq_params)
#
# or_corrected_MFI[:,:,i] = ORC.MFI(or_corrupted[:,:,i], 'im', ktraj, field_map, 2, 1, seq_params)
i += 1
##
# Plot
##
im_stack = np.stack(
(np.squeeze(or_corrupted), np.squeeze(or_corrected_CPR),
np.squeeze(or_corrected_fsCPR), np.squeeze(or_corrected_MFI)))
#np.save('im_stack.npy',im_stack)
cols = ('Corrupted Image', 'CPR Correction', 'fs-CPR Correction',
'MFI Correction')
row_names = ('-/+ 100 Hz', '-/+ 150 Hz', '-/+ 200 Hz')
plot_correction_results(im_stack, cols, row_names)